The field of food concerns all the senses, and its situated aspect is crucial. Activities related to food have a determining social, cultural and ecological context. In "Sensing in Social Interaction, The Taste for Cheese in Gourmet Shops", Lorenza Mondada (professor of linguistics at the university of Basel, Switzerland) delves into the intricate dynamics of sensory experiences within social interactions. The work, divided into four distinct parts, offers a profound exploration of the intersection between the sensorial and the social. The book is organised around two main objectives. The first one is conceptual and questions how we can think about sensoriality as a sociointeractional phenomenon. The second one aims to offer an interactional approach to multisensoriality in situated actions and is based on a field study: tasting and smelling activities of customers and sellers in cheese shops have been recorded in 11 countries and 11 languages (Catalan, Dutch, Finnish, French, English, German, Italian, Portuguese, Spanish, Swedish, Swiss German), producing more than 60 hours of video concerning 600 customers, using multiple cameras, as well as different camera frames.

The first objective is primarily addressed in part I, consisting of two main chapters. The first chapter is purely theoretical, offering a captivating exploration of the anthropology of the senses. This chapter places sensory experiences within the framework of culture, providing a broader perspective. It underscores that reducing our understanding of the senses to what we can measure and observe in humans is incomplete without considering the cultural context. This perspective highlights the potential bias in our exploration of the senses. Personally, I found this chapter to be highly informative about the history of senses and sensory history.

The second objective is covered in parts II, III, and IV, employing a video-based approach to document interactions occurring in a diverse range of gourmet shops involving customers, sellers, and cheese. This approach focuses on capturing language and body gestures frame by frame during the process of selecting and purchasing cheese. These sections are organized sequentially, tracing the sequences that lead to the decision to purchase a specific product. The journey begins with initiating a purchase upon entering the shop (part II, chapters 3 and 4), followed by the exploration of tactile experiences (part III, chapter 5), olfactory sensations (part III, chapter 6), taste perceptions (part IV, chapters 7 and 8), and final assessments (part IV, chapter 9). A conclusion (chapter 10) encapsulates the book's content, emphasizing the distinctive nature of the ethnomethodology and conversation analysis approach in understanding multisensoriality within social interaction. I am a French scientist, particularly interested in the influence of the social context on multisensory integration, and I enjoy indulging in cheese. Therefore, when I saw the title of this book, I was particularly excited. The author rightly states that smell and taste are probably the least studied of the senses in the field of perception and that smell and taste in interpersonal relations are neglected. However, it quickly became clear that the target audience of the book is not scientists in the field of perception. The book's focus on sociointeractional aspects of sensing makes it more suitable for scholars in the social sciences and humanities. The observational approach adopted in the book, which includes ethnomethodology and conversation analysis, clashes with the experimental approach, and dilutes the idea of understanding the underlying processes of perception, which are clearly not the primary focus of the book. In the conclusion, the author wraps up her book saying that it has explored "the contemporary debates that animate the study of the senses across disciplines in the social sciences and the humanities" (p. 510). However, readers familiar with experimental approaches to perception in interaction, such as those discussed by Hale et al. in 2020, may regret that the book does not attempt to bridge the gap to these methodologies. Despite its objectives to bring interdisciplinary research to real life, I felt that the study of the embodied and linguistic dimensions of sensing in interaction could be further enriched by cognitive neuroscience-informed exploration through video recordings, analysing how the body engages and coordinates with other bodies.

While the linguistic reports presented in the book did not align with my specific interests and seemed to represent a clash of disciplines, the surrounding context serves as a valuable source of documentation, providing insights into a variety of scientific cultures. Despite this initial divergence, the first chapter offers significant opportunities for learning. It explores the history of the senses and how language can structure perception. The review of sensory experiences related to food spans across a diverse array of fields, which is both impressive and enlightening. The description of Aristotle's model of the five senses is particularly intriguing and brings a refreshing historical perspective. Furthermore, the exploration of the relationship between language and the senses is highly engaging. It highlights how different cultures vary in the level of detail within their linguistic descriptions of specific senses, and this variation in sensory lexicon is intricately linked to cultural-historical factors and ecological adaptations, rather than merely being a reflection of physiological dimensions.

In the course of my reading, I encountered an issue concerning the accuracy of a reference cited on page 15, which does not align with the content it is intended to support. I was eager to read about how sounds are used to heal in the Malaysian rainforest, but it is not in [START_REF] Roseman | Appraisal determinants of discrete emotions[END_REF] that I will learn about it, as indicated in the book. A simple error like this can be corrected in future editions. The correct source for this information is likely to be found in the book by Back and Bull (2003), which is already cited within the book. Nevertheless, this discrepancy raised concerns about the book's scholarly accuracy and the reliability of its citations.

My own conclusion is that, unfortunately, an ethnological and conversation analytic perspective cannot fully address the complexities of sensoriality, as it tends to confine sensorial experiences within the realm of language. Rather than being solely a book on sensing in social interaction, it encompasses a wide array of concepts related to food and its sensorial dimensions. This includes exploring sociopolitical displacements and tensions related to differences in taste and smell concerning aspects such as immigration, changing consumption patterns, food preparation, preservation, and regulations. The book approaches the intricate web of ramifications in food-related activities, connecting them to cultural meanings, socioeconomic structures, and global historical transformations.

In this sense, although the book diverged from my initial expectations, it contributes significantly to a broader understanding of sensory experiences within social contexts. I would recommend this book to specific audiences in the fields of anthropology and linguistics, as well as to anyone intrigued by the economic dimensions of taste. Potential readers outside these fields, and thus the majority of readers of Perception readers, might find it disappointing that the book doesn't extend its exploration into the realm of food perception from a psychophysical and neuroscience perspective.


PhD Yohanna Michau
email: yohanna.michau@meteo.fr

Aude Lemonsu

Philippe Lucas-Picher

Cécile Caillaud

Gaby Langendijk
email: gaby.langendijk@hzg.de

Sven Kotlarski
email: sven.kotlarski@meteoswiss.ch

Rafiq Hamdi
email: rafiq.hamdi@meteo.be

EVALUATION OF THE URBAN HEAT ISLAND OF 12 CITIES OF FRANCE IN A HIGH-RESOLUTION REGIONAL CLIMATE MODEL SIMULATION

Keywords: Convection-permitting regional climate model, CNRM-AROME, Urban Canopy Model, Town Convection-permitting regional climate model, CNRM-AROME, Urban Canopy Model, Town Energy Balance

In a context of climate change, the evolution of local urban climate conditions could exacerbate the existing environmental and health problems of urban populations. In order to understand the physical processes associated with the urban climate and the potential evolution in local and regional climates, an atmospheric model coupled with a surface scheme including a parametrization of the urban environment can be employed. In this study, the CNRM-AROME Convection-Permitting Regional Climate Model (CP-RCM) coupled to the Town Energy Balance (TEB) module was used at 2.5km of horizontal resolution to assess the model's ability to simulate the atmospheric fields (including minimum and maximum temperatures or precipitations), and the Nighttime Urban Heat Island (UHIN). The simulated urban climate of 12 cities located in metropolitan France is evaluated against different observed datasets on a climatological basis. The model produces some systematic biases on the incoming solar radiation and precipitation, which could explain some biases on surface temperatures. Nevertheless, CNRM-AROME realistically simulates the seasonal variability and the minimum and maximum intensities of UHIN over most cities. Following its satisfactory performance, CNRM-AROME seems a suitable climate model to estimate the future evolution of the urban climate of these cities with climate change.

INTRODUCTION

Urban areas are highly artificial environments, with high 3-D structures [START_REF] Kuttler | The Urban Climate -Basic and Applied Aspects[END_REF], and few natural and plant-covered surfaces. These characteristics modify the surface radiation and energy exchanges, that especially increase storage of heat [START_REF] Oke | Urban climates[END_REF] and initiate the well-known Urban Heat Island (UHI) phenomenon, resulting in higher temperatures in the city than in surrounding rural areas. Surface imperviousness, and urban infrastructures and networks modify local water flows, increase the surface runoff, and reduce evaporation. In addition, combination of surface roughness and warming induce thermal and dynamic turbulence that disturbs the near-surface airflow and dynamics of the atmospheric boundary layer.

The above urban characteristics give rise to a specific urban climate that changes the street-level environmental conditions, but also interact with meteorological and climatic phenomena at local and regional scale, and most of the time enhance high-impact weather hazards [START_REF] Masson | Urban Climates and Climate Change[END_REF]. Cities can influence wind speed and the direction of synoptic flow, and modulate local atmospheric circulations [START_REF] Childs | Observations and Numerical Simulations of Urban Heat Island and Sea Breeze Circulations over New York City[END_REF][START_REF] Lemonsu | The New Canadian Urban Modelling System: Evaluation for Two Cases from the Joint Urban 2003 Oklahoma City Experiment[END_REF][START_REF] Bauer | Interaction of Urban Heat Island Effects and Land-Sea Breezes during a New York City Heat Event[END_REF]. They may increase the persistence of cloud cover by advecting moisture [START_REF] Theeuwes | Persistent cloud cover over mega-cities linked to surface heat release[END_REF], and enhance the development of convective cloud (Shimadera et al., 2014). Studies have also shown a change in the frequency and intensity of thunderstorms [START_REF] Kingfield | Effects of City Size on Thunderstorm Evolution Revealed through a Multiradar Climatology of the Central United States[END_REF]Ashley et al., 2012), and an increase of precipitation over urban areas during summer [START_REF] Liu | Meta-analysis of urbanization impact on rainfall modification[END_REF][START_REF] Roy | Long time series spatialized data for urban climatological studies: A case study of Paris, France[END_REF]. In turn, the urban climate is influenced by regional weather and climate. Especially, even if UHI is strongly governed by the characteristics of the city (size, population, urban morphology, and materials), the UHI also depends on the geographical context and is modulated by the atmospheric conditions. Both daily variability and annual pattern of UHI are influenced by solar irradiance, wind speed, cloud cover, and precipitation [START_REF] Oke | Urban climates[END_REF].

As the consequence of local urban climate and UHIs, cities are already facing environmental issues and causing health problems to their inhabitants. For instance, the excess of mortality recorded in Paris in 2003 has been partly attributed to the nocturnal UHI and extreme heat [START_REF] Laaidi | The Impact of Heat Islands on Mortality in Paris during the August 2003 Heat Wave[END_REF]. In a changing climate context, these issues and related impacts could change and be enhanced. The anticipated evolution of temperature is expected to modify the energy consumption linked to the use of air conditioning in summer and heating in winter (Shourav et al., 2018). In addition, the increase in the occurrence of heatwaves and their intensification -known to be amplified in cities by UHI [START_REF] Tan | The urban heat island and its impact on heat waves and human health in Shanghai[END_REF] -could affect more extensively the thermal comfort of urban populations, and lead to serious health effects.

The study of the urban climate of large cities and of its future evolution requires specific climate modelling tools and methodologies. As discussed, it is crucial to consider the surface characteristics of cities and the associated physical processes, and to deal with the complex interactions with local and regional atmospheric processes. However, the horizontal resolution of climate models is often a major limitation because it is too coarse to represent urban areas. Supercomputer performances now allow to perform decadal regional climate simulations using finer horizontal resolutions up to one or a few kilometers [START_REF] Kendon | Challenges and outlook for convection-permitting climate modelling[END_REF]. These configurations are based on Convection-Permitting Models (CPM), so called because the resolution makes it possible to explicitly resolve deep convection and associated precipitation [START_REF] Prein | A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges[END_REF]. A scientific challenge of this new generation of climate model often based on numerical weather prediction models consist in reducing systematic biases, in preserving energy and water balance in long-term climate integrations, and in considering varying greenhouse gases (GHG) and aerosol concentrations [START_REF] Lucas-Picher | Convection-permitting modeling with regional climate models: Latest developments and next steps[END_REF]. The finer resolution of CPMs allows a more accurate discretization of equations, and thus, a better simulation of the atmospheric circulation and gradients [START_REF] Lucas-Picher | Evidence of added value in North American regional climate model hindcast simulations using ever-increasing horizontal resolutions[END_REF]. In addition, CPMs provide a step change in the representation of complex orography [START_REF] Woodhams | What Is the Added Value of a Convection-Permitting Model for Forecasting Extreme Rainfall over Tropical East Africa[END_REF], characteristics of surface fields (including coastal regions), and in the representation of small-scale phenomena such as sea breezes, local winds, and tropical cyclones [START_REF] Shu | Added value of convection permitting climate modelling in urban overheating assessments[END_REF]. The dynamical downscaling offered by CPM is particularly appealing for city-scale effects and impact studies. Due to the distinctive characteristics of the urban environment, the simulation of urban climates can be done by coupling an atmospheric model with an Urban Canopy Model (UCM), such as the Town Energy Balance (TEB) [START_REF] Masson | A Physically-Based Scheme for the Urban Energy Budget in Atmospheric Models[END_REF]. In the last decade, urban parameterizations in land surface schemes of regional climate models have been used to investigate the urban climate [START_REF] Hamdi | The State-of-the-Art of Urban Climate Change Modeling and Observations[END_REF]. [START_REF] Mccarthy | Simulating climate change in UK cities using a regional climate model, HadRM3[END_REF] studied the evolution of the UHI intensity under a changing climate with the regional climate model HadRM3 at 25 km of resolution coupled to a bulk parameterization. In the same way, Daniel et al. (2017) underlined the added value of modelling the urban climate by activating TEB in the RCM CNRM-ALADIN at 12.5 km over Paris, France. At a finer scale, [START_REF] Mussetti | Simulating urban climate at sub-kilometer scale for representing the intra-urban variability of Zurich, Switzerland[END_REF] investigated the potential of using COSMO-CLM coupled with the multi-layer UCM Double-Canyon Effect Parameterization (DCEP) to simulate the heat wave of June-July 2015 over the city of Zurich, Switzerland. Instead, other studies employed a statistical and/or dynamical downscaling to explore urban climate. For instance, urban dynamical downscaling technique has been applied by [START_REF] Lemonsu | Evolution of the Parisian urban climate under a global changing climate[END_REF] and [START_REF] Hamdi | Evolution of urban heat wave intensity for the Brussels Capital Region in the ARPEGE-Climat A1B scenario[END_REF] by running offline climate simulations.

In this study, the French numerical weather prediction model AROME adapted to long-term climate simulations (then called CNRM-AROME) and coupled with the TEB urban canopy model is used at 2.5 km of resolution. A simulation was performed on an extended France domain (northwest of Europe) as part of the European Climate Prediction system (EUCP) project for the period [START_REF] Masson | A Physically-Based Scheme for the Urban Energy Budget in Atmospheric Models[END_REF]-2018[START_REF] Lucas-Picher | Evaluation of the convection-permitting regional climate model CNRM-AROME41t1 over northwestern Europe[END_REF]. Previous CNRM-AROME simulations centered over the Paris region allowed a specific analysis of the Parisian climate and an evaluation of the added value of this configuration compared to the regional climate model CNRM-ALADIN with a horizontal resolution of 12.5 km [START_REF] Lemonsu | What added value of CNRM-AROME convection-permitting regional climate model compared to CNRM-ALADIN regional climate model for urban climate studies? Evaluation over Paris area (France)[END_REF]. Here, the scientific interest consists in evaluating the UHI in the CNRM-AROME hindcast simulation over 12 large and medium-sized French cities located in contrasted climatic and geographic areas. The originality of this study relies on the multi-city analysis with a generic methodology. Section 2 presents the numerical models and observational databases used for this study, as well as the proposed methodology for city selection and model assessment. Section 3 presents the results, first the general evaluation of the climatic variables simulated in the vicinity of the cities, and then the evaluation of the modelled nighttime UHI. Section 4 provides a discussion of the chosen evaluation method, before conclusions in Section 5.

MATERIAL AND METHODS

2.1.

Model descriptions and configurations [START_REF] Seity | The AROME-France Convective-Scale Operational Model[END_REF]. CNRM-AROME is a non-hydrostatic, bi-spectral, and limited-area model using a 2.5 km horizontal resolution and 60 vertical levels to describe the atmosphere from 10 m to 1 hPa, with a higher density of levels in the atmospheric boundary layer. It has a semi-Lagrangian advection scheme and a semi-implicit temporal discretization algorithm.

This study is based on the CNRM-AROME simulations performed in the framework of the European research project EUCP [START_REF] Hewitt | Toward a European Climate Prediction System[END_REF] on a northwestern European domain of 720 x 900 grid points. The simulations cover the period January 1, 2000, to December 31, 2018 (after a 2-year spin-up) following a hindcast configuration: the model is forced every hour at its lateral boundaries by the regional climate model CNRM-ALADIN (Aire Limitée Dynamic Adaptation International Development -Nabat et al., 2020) with a horizontal resolution of 12.5-km, which is itself driven by the ERA-Interim reanalysis with a horizontal resolution of 80-km.

Land surface modelling system

The CNRM-AROME is coupled with the SURFEX land surface modelling platform [START_REF] Masson | The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes[END_REF]. Each cell of the simulation domain grid is described as the combination of the four cover types: natural surfaces (soils and vegetation), urban areas, continental waters (lakes, rivers, etc.) and ocean waters (sea or ocean). The functioning of these covers and their exchanges with the atmosphere are calculated independently by different and dedicated models integrated into the SURFEX platform.

More specifically, the Interaction Soil-Biosphere-Atmosphere model (ISBA - [START_REF] Noilhan | A Simple Parameterization of Land Surface Processes for Meteorological Models[END_REF][START_REF] Noilhan | The ISBA land surface parameterisation scheme[END_REF] resolves the water and energy transfers between natural soils and vegetation and the atmosphere. For the CNRM-AROME configuration, the ISBA is used in its 3-L version [START_REF] Boone | Inclusion of a Third Soil Layer in a Land Surface Scheme Using the Force-Restore Method[END_REF] that discretizes the soil column with three hydrological layers i.e., a surface layer, a root layer, and a deep layer. The vertical water exchanges between layers are calculated according to the Force-Restore Scheme [START_REF] Deardorff | Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation[END_REF].

The urban areas are treated by the Town Energy Balance model (TEB - [START_REF] Masson | A Physically-Based Scheme for the Urban Energy Budget in Atmospheric Models[END_REF]. It is based on the canyon concept developed by [START_REF] Oke | Boundary layer climates[END_REF], in which the city is represented at each grid cell of the simulation domain by an average urban canyon composed by a flat roof, a road and two walls of the same height facing each other.

TEB solves the radiation, energy and water balance for each urban canyon element, then the exchanges between the urban canopy layer and the atmosphere. As an urban canopy model, TEB addresses the main physical processes related to the urban geometry and to the thermal, radiative and water properties of artificial materials: shading effects in the canyon, solar and infrared radiation trapped by multiple reflections, large heat storage in urban infrastructure, drag effect of buildings on street-level airflow, soil sealing and surface runoff. The near-surface air temperature is diagnosed in TEB within the canyon at mid-height of buildings (see details in [START_REF] Masson | A Physically-Based Scheme for the Urban Energy Budget in Atmospheric Models[END_REF].

Land use database

The surface description comes from the global ECOCLIMAP database, based on the Corine Land Cover database (Buttner, 2002) over Europe. ECOCLIMAP provides a map of the land use land cover classes with a 1-km horizontal resolution, as well as the associated surface properties as input to surface models [START_REF] Champeaux | ECOCLIMAP: a global database of land surface parameters at 1 km resolution[END_REF]. CNRM-AROME uses the version ECOCLIMAP-I [START_REF] Masson | A Global Database of Land Surface Parameters at 1-km Resolution in Meteorological and Climate Models[END_REF] that consists in 243 classes, including many classes to describe areas of crops, grasslands, forests, as well as ten urban classes (e.g., dense urban areas, peri-urban areas, industrial and commercial areas, airports, urban parks). For vegetation classes, the main descriptive parameters required by ISBA are the fraction of plant function types (PFT), leaf area index, tree height, stomatal resistance, root fraction profile etc. For urban classes, the main descriptive parameters are the impervious cover fraction, building density, wall surface density, mean building height, radiative and thermal material properties of roofs, roads, walls.

The ECOCLIMAP data is interpolated on the CNRM-AROME grid to obtain fractions of land use land cover classes in each grid cell. This makes possible to determine the mask of the four main cover tiles, and to derive average surface parameters from the parameters of each class weighted according to their respective cover fraction.

Observational datasets

Several datasets based on observations were used for this study at different stages of the model evaluation.

They include both gridded observation products and weather stations, with different time periods that are long enough to allow a robust comparison (at the climatological scale) of the modelling outputs.

Long time series of gridded weather data

The ANASTASIA (Analyse Spatiale des Températures de Surface avec Initialisation AURELHY) product is a gridded dataset of minimum (TN) and maximum (TX) daily temperature at 1-km horizontal resolution over Metropolitan France for the time period 1947-2016 [START_REF] Besson | Climate monitoring and heat and cold waves detection over France using a new spatialization of daily temperature extremes from 1947 to present[END_REF]. It uses a regression-kriging method (Hengl et al., 2007) with two sources of information: in situ observations of extreme daily TN and TX recorded by the weather stations of the operational Météo-France network and a monthly climatological field of daily TN and TX defined over the 1982-2010 period [START_REF] Cannelas | Les normales climatiques spatiallisées Aurelhy 1981-2010: températures et précipitations[END_REF]. This method includes a digital terrain elevation model that considers the influence of the topography on the spatial variability of temperature.

The COMEPHORE is a gridded precipitation dataset produced by merging radars and rain gauges from the French meteorological network [START_REF] Tabary | 2006) reanalysis of quantitativeprecipitation estimation over France[END_REF]. It provides hourly cumulative precipitation at a 1-km horizontal resolution for the time period 1997-2021.

Weather stations

Given the substantial underestimation of the incoming solar radiation (bias of the order of -6 to -10 W.m -2 ) underlined by [START_REF] Vidal | A 50-year high-resolution atmospheric reanalysis over France with the Safran system[END_REF], we decided to not use the conventional SAFRAN analysis product available over France on a regular 8-km resolution grid as a reference of this evaluation, Thus, the evaluation of incoming solar radiation was carried out using station data from the GEBA (Global Energy Balance Archive) network. The GEBA database produced by ETH Zurich, Switzerland [START_REF] Wild | The Global Energy Balance Archive (GEBA) version 2017: a database for worldwide measured surface energy fluxes[END_REF] provides long time series of monthly incoming radiation from station measurements on the Earth's surface. Eleven GEBA stations available on the French territory were operated, each of them near one of the cities of interest. One station of the Météo-France operational network was used to complete the incoming solar radiation dataset for one of the cities not covered by the GEBA network.

The ANASTASIA method presented before under-estimates the spatial influence of cities on the retrieved TN/TX maps because few stations are truly located in urban areas. To complement the dataset and assess the UHI of each city, long time series of TN and TX were selected and extracted from weather stations of the French operational meteorological network that is the closest to each urban center.

Three-step methodology for the model evaluation

To address the general objective of analyzing the ability of the CNRM-AROME model to simulate the characteristics of the urban climate on the French metropolitan territory, this study follows a three-step methodology. First, a set of 12 large and medium-sized French cities is selected. Then, the overall performances of the model are evaluated with respect to the local climate conditions of each area of interest i.e., in the surrounding environment of the selected cities. Finally, the evaluation and comparison of the UHI simulated by the CNRM-AROME model for the different cities are performed.

Selection of the cities

The city selection is based on two main criteria, which are ( 1) the availability of long series of temperature measurements (TN and TX) for a weather station located in the urban area of each city and covering the time period 2000-2018; and ( 2) contrasting climatic and geographical contexts. Twelve French cities among the main cities of Metropolitan France were selected (Annex 1 and Figure 1) and grouped according to their climatic zones (based on the description of [START_REF] Joly | Les types de climats en France, une construction spatiale[END_REF].

The Annex 1 presents, for the 12 cities, the 19-year climate normals of temperature, precipitation, and incoming solar radiation calculated using ANASTASIA, COMEPHORE, and GEBA or Météo-France weather stations, respectively.

-Lille, Nantes, and Rennes are located on the northern half of France, near the Atlantic coast. They are subject to an oceanic climate characterized by the lower annual thermal contrasts (less than 13C difference between January and July climate normals), and average annual temperature of 10.8 °C, 12.5 °C and 12.1 °C respectively. Moreover, climate normals over 1981-2010 highlights a small number of cold and hot days, and abundant precipitation of more than 690 mm per year. -Bordeaux, also classified as an oceanic climate, is located on the southern half of the Atlantic coast. The average annual temperatures of about 13.8 °C and a cumulative rainfall of 944 mm per year. -Paris and Toulouse are characterized by a degraded oceanic climate with warmer mean annual temperature of above 12 °C, hot summers and mild winters, and an average rainfall accumulation of less than 650 mm per year. -Montpellier, Nice, and Nîmes are in southeastern France near the Mediterranean coast. They are characterized by a Mediterranean climate with high average annual temperatures (15.1 °C, 16 °C and 15.2 °C respectively), associated with few cold days and frequent hot days. Rainy episodes in this region are rare (around 60 days with precipitations over 1 mm per year), but very intense, responsible for frequent floods particularly in autumn. On average, total rainfall is quite high with around 700 mm per year. -Located in eastern France, Nancy, Dijon, and Strasbourg are characterized by a continental climate with hot summers, and harsh winters with many days of snow or frost. The average annual temperature is less than 11 °C and total rainfall is between 665 and 846 mm per year.

Based on the analysis of climate normals in Annex 1, similarities between cities are underlined. Cities are finally grouped according in four climate zones (Figure 1), based on Joly et al. ( 2010): the continental climate (CON, Nancy, Dijon, Strasbourg), the Mediterranean climate (MED, Montpellier, Nice, Nîmes), the oceanic climate (OCE, Bordeaux, Lille, Nantes, Rennes), and the semi-oceanic climate (SOCE, Paris and Toulouse). Figure 1 shows the location of the 12 cities with the delimitation of the climatic zones and the orography. Additional information on cities is available in Table 1. Table 1. Additional informations about cities. From left to right, the name of the city, the latitude and longitude of the very city center, the altitude (in m), the surface of urban area (in km²), the population living within the agglomeration (x 1000 inhabitants), the environment of the urban weather station and its distance to the city center (in km). The urban climate, and in particular the UHI, evolves on a daily basis depending on local weather (sunlight, wind, precipitation etc.). The quality of UHI simulated by CNRM-AROME therefore depends (among other factors) on its ability to simulate realistic climate conditions in the vicinity of cities. The first objective of the general evaluation focuses on precipitation, incoming solar radiation, and temperature analysis.

Simulated precipitation and temperature are evaluated based on the gridded COMEPHORE and ANASTASIA datasets, respectively. These long time series of gridded data based on weather stations are interpolated on the CNRM-AROME 2.5-km grid using a first-order conservative remapping. For each city, the model outputs are compared to COMEPHORE and ANASTASIA over a reference geographical domain that is centred on the city with a spatial coverage including the urban area and the surrounding countryside. As Paris is a large-size city, the selected domain covers a 100 km radius circle around the city center, while the radius used for the other cities are 50 km. For each domain, a spatial average is calculated both for model outputs (xMod) and observations (xObs), with x the metric (TN, TX, daily precipitation).

The GEBA and the Météo-France weather stations are used for the evaluation of simulated incoming solar radiation using a grid point approach. The simulated global incoming radiation comes from the combination of the direct and diffuse components of the shortwave incoming radiation extracted from the CNRM-AROME outputs at the closest grid point to the location of the station selected for each city.

The comparison between the CNRM-AROME and observations is performed by calculating for each season the mean bias (Equation 1-for TN and TX, in °C) or the percent bias (Equation 2-for daily precipitation and incoming solar radiation, in %):

𝑀 𝑏𝑖𝑎𝑠 = 1 𝑛 × ∑ (𝑥 𝑀𝑜𝑑 𝑖 -𝑥 𝑂𝑏𝑠 𝑖 ) 𝑛 𝑖 = 1
Equation 1

𝑝 𝑏𝑖𝑎𝑠 = 1 𝑛 × ∑ ( 𝑥 𝑀𝑜𝑑 𝑖 - 𝑥 𝑂𝑏𝑠 𝑖 𝑥 𝑂𝑏𝑠 𝑖 ) 𝑛 𝑖 = 1 × 100 Equation 2
and the root-mean square error (Equation 3, in the unit of the metric):

𝑅𝑀𝑆𝐸 = √ ∑ (𝑥 𝑀𝑜𝑑 𝑖 -𝑥 𝑂𝑏𝑠 𝑖 ) 2 𝑛 𝑖 = 1 𝑛 Equation 3
xMod et xObs correspond either to the spatial average of temperature or precipitation, or to the closest grid point corresponding to the location of the GEBA station. The scores are calculated based on time series, with n the number of values over the period of interest (with of daily time step for all metrics, except radiation that is available monthly).

Multi-city evaluation of the urban heat island

The second step of the evaluation addresses the urban aspects. The focus is on the climatological assessment of the nighttime UHIs simulated by the CNRM-AROME for the 12 selected cities (located in contrasting climatic zones). The Urban Heat Island Nighttime (UHIN, in °C) is calculated both for observations and model outputs according to:

𝑈𝐻𝐼𝑁 = ∆𝑇𝑁 = 𝑇𝑁 𝑈𝑅𝐵 -𝑇𝑁 𝑅𝑈𝑅 Equation 4
For each city, the urban temperature (TNURB) refers to the minimum daily temperature (TN) time series recorded by the urban weather station or simulated by CNRM-AROME over the period 2000-2016 (cf. Section 2.2). A systematic check is done to ensure that the CNRM-AROME urban grid point is properly selected. The closest point to the station is chosen, with an urban fraction of the cell that is greater than 50% and a water fraction (sea, oceans, lake, river, etc.) lower than 5%.

The rural temperature (TNRUR) is the observed or simulated spatial average of TN time series over the reference geographical domain centered over the city. A mask is used to identify all rural grid points of the domain, i.e., for which the fraction of natural covers in the cell is greater than 50% based on the ECOCLIMAP database (cf. Section 2.1.3). An additional check is implemented on elevation to make sure that the rural grid points for which the difference in altitude with the urban area greater than 750 m are excluded from spatial averaging.

This generic method is shown in a case study focusing on Nice, located in the southeastern France. The local topography is very contrasted due to the proximity of the Mediterranean Sea (in the south) and the Mercantour Massif (in the north, ZS > 2500 m) (Figure 2, left). The topography increases quickly moving away from the Mediterranean coast, even at 2.5-km of horizontal resolution (Figure 2, left). The check on elevation consists of hiding high altitude grid point in order to limit the temperature gradient in rural areas (between the sea front and the inland). TNRUR is calculated using the remaining grid points composed by crops or forest (Figure 2, right). Initially, the closest urban grid point to the urban station corresponds to water (station's grid point on Figure 2). The land covers of the surrounding grid points are analyzed using the generic method to select the closest urbanized cover as the new urban grid point (selected grid point on Figure 2, right).

RESULTS

Model evaluation for local-scale climate conditions

3.1.1. Rainfall The indicators of Table 2 underline a seasonal and geographical variability of model climatological performances. In general, for the oceanic, semi-oceanic, and continental cities, the precipitation is overestimated by CNRM-AROME, severely in DJF (winter, by 20-33%), more severely in MAM (spring, by 31-56%), and moderately in SON (autumn, up to 15% maximum), and they are underestimated in JJA (summer, down to -21% maximum). The orders of magnitude given here do not include the results of Strasbourg, which show particularly degraded local performance, with precipitation overestimated throughout the year by 21 to 73%. For the Mediterranean cities that experience a particular precipitation regional climatology, CNRM-AROME performs differently with a tendency to underestimate low rainfall. In DJF, and especially in JJA, a precipitation deficit simulated by CNRM-AROME is noted (down to -45% for Nîmes in JJA). The simulated precipitation is very similar to that observed in SON. However, the precipitation is overestimated in MAM as over the rest of the country, but with much smaller biases (< 20%). Bordeaux and Toulouse present similar findings with -30.7 and -20.8% respectively.

Incoming solar radiation

Table 3 shows the 2000-2018 seasonal mean bias and RMSE of incoming solar radiation simulated by CNRM-AROME compared to the GEBA and the Météo-France weather stations. CNRM-AROME systematically overestimates the incoming solar radiation for all seasons and cities. Percent biases and RMSE are greater for continental cities (Dijon, Nancy, Lyon, and Strasbourg) than for the other ones, and particularly high in DJF and SON (biases of +22 to 35%). Percent biases range from +4 to 15% for oceanic cities, +6 to 23% for semi-oceanic cities, and +6 to 18% for Mediterranean cities. CNRM-AROME simulates realistic seasonal mean TN for northwestern cities, especially in MAM and SON. For Lille and Rennes, seasonal biases are less than ± 0.5 °C for all seasons and RMSE does not exceed 1 °C. The model performs quite well for Nantes and Paris, except in JJA when a little warm bias is noted. The model overestimates TN for the other cities for all seasons, but with geographical variability. Cities located on the Strasbourg-Toulouse diagonal (including Nancy and Dijon) have biases and RMSE less than 1 °C in DJF, MAM, SON, and higher bias in JJA (between +1 and +3 °C). Biases and RMSE are maximum for Bordeaux, Montpellier, Nice and Nîmes (above +0.5 °C for all seasons). This overestimation of TN simulated by CNRM-AROME is particularly severe in summer where the biases exceed 3 °C, over the Mediterranean coastal areas.

Regarding maximum temperature, southern cities such as Bordeaux, Nîmes, and Toulouse have severe positive biases in JJA (between +0.5 and +2 °C). Otherwise, CNRM-AROME model tends to underestimate TX for other seasons. These cold biases are low in SON, a slightly larger in winter (below -1 °C for all cities). In MAM, CNRM-AROME systematically underestimates TX (between -4 and -2 °C).

Climate assessment of nighttime UHI

Comparison of observed nighttime UHI

This section aims to evaluate the ability of CNRM-AROME to simulate the urban climate characteristics for each city with a particular attention to the nighttime UHI (referred to as UHIN). First, the monthly climatology of UHIN determined for each city from observations over 2000-2016 are analyzed and compared (Figure 4). UHIN intensities vary largely between the twelve cities of interest, from 0 to +2.8 °C for wintertime and from +0.6 to +3.6 °C for summertime. The UHIN seasonal variability differs from one city to another, but mainly displays maximum intensities between April and September and minimum intensity between December and January. Similarities between UHIN of some cities are underlined in relation to the climatic zones they belong to.

Assessment of simulated nighttime UHI

The monthly climatology of simulated UHIN, calculated following the same method as observation, is presented in Figure 4. In addition, Figure 5 shows the seasonal biases computed for UHIN over 2000-2016 for the 12 cities of interest. For all continental (Dijon, Nancy and Strasbourg) and semi-oceanic cities (Paris and Toulouse), as well as for Lille (OCE) and Nîmes (MED), the UHIN monthly climatology simulated by CNRM-AROME is close to that derived from observations (Figure 4), even though the maximal intensity is slightly underestimated by the model in most of cities. The biases are less than ± 0.5 °C for all seasons for these cities (Figure 5). For Nantes and Rennes (OCE),

UHIN is overestimated by +0.5/1 °C for all seasons (Figure 5) but the seasonal variability is quite well reproduced (Figure 4). Bordeaux, classified as oceanic but located more to the south than others, presents cold biases especially in JJA. The same is true for Montpellier and Nice (MED). More generally, for these three cities (and for Nîmes to a lesser extent), the monthly evolution of UHIN (Figure 4) shows a strong weakening in spring and summer, while it strengthens in observations.

As conclusion, for most of the cities of interest, CNRM-AROME correctly simulates both intensity and seasonality of UHIN, and rather well captures the variations of UHIN between the different climatic zones. Nevertheless, the comparisons highlight two defect of the model ( 1) in the simulation of summertime UHIN for southern coastal cities (Bordeaux, Montpellier and Nice), and ( 2) to a lesser extent in the simulation of UHIN's general intensity for Nantes and Rennes (OCE). The different origins of theses biases are discussed below.

DISCUSSION

Specificities of southern coastal cities

For the coastal cities of Bordeaux, Montpellier, and Nice (as well as Nîmes to a lesser extent), a mitigation of UHIN is simulated in spring and summer, contrary to the observed seasonal trends. This results from a strengthening of the warm bias in the surroundings of the city from May to September (see TNRUR in Figure 6). The identified biases are investigated more in detail with Montpellier as a case study, using additional observations available for analysis in the vicinity. The Météo-France weather station of Prades-le-Lez (latitude: 43.7183, longitude: 3.8666) provides long series of near-surface hourly wind speed and direction measurements, which makes it possible to study how local sea-breeze circulations are represented by CNRM-AROME. In addition, the Puéchabon flux station (latitude: 3.5957, longitude: 43.7413), part of the international FLUXNET2015 database [START_REF] Pastorello | The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data[END_REF], located in a holm oak forest about 20 km northwest of Montpellier, provides long-term time series of turbulent heat fluxes. This site is useful to investigate the land surface energy exchanges. In coastal regions, land-breezes and sea-breezes can be established during the night and day, respectively, in response to temperature differential between the continental and maritime areas. By transporting warmer or colder air masses depending on the direction, these local circulations can influence the surface energy exchanges and therefore the near-surface temperatures. A wrong representation of coastal effects in CNRM-AROME could explain some biases. Figure 7 shows the observed and simulated summer wind rose at 18 UTC for Montpellier surroundings. A variable distribution of wind direction in noted in the observations with two predominant regimes: (1) a moderate north-westerly flow from the land, with speeds of 4 m/s or more; and (2) a weaker southerly flow corresponding to the sea breeze. In comparison, CNRM-AROME underestimates the occurrences of sea-breeze circulations until the end of the day. For example, the observations indicate occurrences of southerly flows exceeding 25% against only 10% in the model. This suggests that inland areas are not cooled enough in the simulation by the penetration of the airflow coming from the sea. This could contribute to the warmer temperatures noted in the rural environment and consequently to the overestimation of UHIN. Note that the origin of this under-representation of breeze circulations in the model has not been explored in more detail here but could be the result of a bias in the sea surface temperatures that are prescribed in CNRM-AROME from a climatology.

In addition, the general evaluation of CNRM-AROME highlighted that the region was subject to a deficit of precipitation (Section 3.1.1) combined to an excess of incoming solar radiation (Section 3.1.2) in the summer period compared to what is observed. It is expected that the local climatic constraints will influence the surface energy fluxes. This is investigated by comparing the evaporative fraction (EF, in %) calculated from the turbulent fluxes measured by the Puéchabon station and simulated at the corresponding grid point. This term reflects the fraction of available energy allocated to evapotranspiration and is calculated from the daytime values of sensible (QH) and latent (QE) turbulent heat fluxes according to: The simulated daily precipitation and UHIN are also displayed. The observed EF remains steady around 40% in winter, slightly increases until June to reach 45%, and then decreases during summer down to 30% in August, before increasing again until November. CNRM-AROME exhibits a larger range of monthly EF: between October and May, EF remains greater than 55%, and then decreases drastically from June to September with a minimum of 20% in August. This severe decrease in evapotranspiration in the rural environment is in line with the dry period indicated by the sharp decrease in rainfall simulated by CNRM-AROME (down to less than 0.5 mm per day in July) combined with a very high level of insolation. These conditions favor the soil drying, which could contribute to an excessive rise in rural temperature and result in a simultaneous decrease in UHIN (as noted in Figure 8).

𝐸𝐹 = 𝑄 𝐸 𝑄 𝐸 +𝑄 𝐻 Equation 6

Methodological limitation

The Figure 9 compares observed and simulated urban (TNURB) and rural (TNRUR) temperatures, which are used to calculate UHIN of Nantes and Rennes. For both cities, observed TNURB and TNRUR are quite close throughout the year for both cities. CNRM-AROME simulates urban temperatures systematically higher than that observed, when rural temperatures are relatively close to observations, resulting in an overestimation of UHIN. This warm bias can be partly attributed to the generic method for calculating and comparing observed and simulated UHIN, due to the potential bias in both the representativeness of the station chosen as the urban reference and the model grid point selected for the comparison. As a reminder (see Section 2.3.3), the CNRM-AROME grid cell selected as the urban reference is the closest one to the urban station, with at least 50% of urban coverage and less than 5% of water coverage. In the case of small and medium-sized cities, such as Nantes and Rennes, the whole urban area is covered by only a few CNRM-AROME grid cells. According to the method, the selected one is necessarily close to the most central and urbanized grid cell of the city. It is representative of the sub-urban area, whereas the grid cell corresponding to the actual location of the urban reference station is not very urbanized and mostly occupied by croplands in Rennes as shown in Figure 10 (left), which may explain warmer modelled temperature than the observed one.

The low availability of long observation series, especially in urban areas where weather stations are scarce [START_REF] Varentsov | Simultaneous assessment of the summer urban heat island in Moscow megacity based on lessigreaterin situless/igreater observations, thermal satellite images and mesoscale modeling[END_REF], is another limitation. The best compromise, for a common methodology for all cities, was to use weather stations of the Météo-France network, although those classified as urban are mostly located on the outskirts of the city center, on airports or airfields (Table 1) i.e., in suburban areas. It suggests that minimum temperature is lower there than in the very city center, and thus that UHIN is weaker than expected. For some years now, some French cities have been installing permanent urban networks for UHI monitoring, as in Toulouse [START_REF] Dumas | Co-construction of climate services based on a weather stations network: Application in Toulouse agglomeration local authority[END_REF], Dijon [START_REF] Richard | How relevant are local climate zones and urban climate zones for urban climate research? Dijon (France) as a case study[END_REF], Rennes [START_REF] Dubreuil | Fréquence et intensité des îlots de chaleur à Rennes : bilan de 16 années d'observations (2004-2019)[END_REF]. To evaluate and discuss the relevance of our results and methodology, the data from the Rennes network (which is the oldest in operation) could be compared on a monthly basis with Météo-France and CNRM-AROME data over the common period 2004-2016. Figure 10 (left) . Figure 10 (left) presents the mean annual cycle of UHIN calculated three ways: (1) from the city center station of the Rennes urban network (OBS-REN), ( 2) from the MétéoFrance station (OBS-MF) and (3) from the most urbanized grid cell of CNRM-AROME over Rennes (MOD-REN). As previously noted for Rennes and Nantes, even from the data of urban network, the seasonality of UHIN is found weak due to oceanic conditions and UHIN is stable all over the year varying between 2 and 3 °C for city centre (OBS-REN). In comparison, both monthly variability and intensities of UHIN are significantly lower for OBS-MF, that is explained by the less urban local conditions of the Météo France station. Finally, CNRM-AROME compares well to OBS-REN in terms of variability but with slightly weaker intensities. For a small-sized city as Rennes, the horizontal resolution of 2.5-km is somewhat loose to accurately capture the characteristics of the city centre of Rennes which covers a smaller area. Conversely, the grid point considered is more urbanized than the environment of the Météo France station, hence an overestimation of the UHIN with MOD-REN when compared to OBS-MF. It can therefore be considered that CNRM-AROME gives an intermediate estimate of UHIN representative of the peri-urban areas.

City size and model resolution

Finally, the ability of the model to reproduce the intensity and spatial extent of the UHIN is also largely dependent on the size of the city at the 2.5-km horizontal resolution. For illustration purpose, Figure 11 is a mapping of the mean summer UHIN over 2000-2016 over Dijon which is a small-sized city, Toulouse which is a medium-sized city, and Paris which is by far the largest urban area in metropolitan France. For Dijon (Figure 11, left), the maximum intensity is reached in the historical center, varying between +2 and +2.5 °C, which is slightly lower than that found by [START_REF] Richard | How relevant are local climate zones and urban climate zones for urban climate research? Dijon (France) as a case study[END_REF] based on local observations but over a shorter time period. In addition, the complex topography of the Dijon region results in a local temperature gradient that is superimposed on the urban effects. For Toulouse (Figure 9, middle), the summertime UHIN intensity is quite small (around +1 and +1.5 °C) in the CNRM-AROME simulation but its spatial pattern is more extended and follows the shape of the city. These results are slightly higher than the findings of [START_REF] Houet | Mapping urban climate zones and quantifying climate behaviors -An application on Toulouse urban area (France)[END_REF], which quantified a UHIN of 2 ± 0.3 °C between UCZ2 (i.e., old city core) and UCZ6 (i.e., airport) in Toulouse. Finally for Paris (Figure 9, right), a more pronounced and extended UHIN is simulated (with maximum intensity between +3.5 and +4 °C in the very city center) since the urban area is larger and more densely built up. In this case, the horizontal resolution of 2.5 km is sufficient to describe the pattern of the UHI which covers Paris and its inner suburbs, but also follows the urbanized valleys to the southeast and northwest (as shown by [START_REF] Lemonsu | What added value of CNRM-AROME convection-permitting regional climate model compared to CNRM-ALADIN regional climate model for urban climate studies? Evaluation over Paris area (France)[END_REF].

CONCLUSION

A hindcast simulation performed with the CP-RCM CNRM-AROME with a 2.5-km horizontal resolution was analyzed to investigate the potential of this new generation of climate models for urban-oriented studies. The ability of the model to simulate the urban heat island was evaluated for twelve French cities of different sizes and located in contrasting climatic contrasting climatic zones and geographical contexts.

A common evaluation methodology was applied to each city based on the exploitation of national gridded datasets and long time series of TN/TX collected by weather stations of the French weather monitoring network. The local atmospheric conditions (precipitation, incoming solar radiation, and temperature), that modulate UHIN on both daily and seasonal scales, were first qualified in the vicinity of the cities. Model performances for the simulation of precipitation are geographical and seasonal dependent. For most cities, precipitation is overestimated in DJF, MAM and to a lesser extent in SON, and underestimated in JJA. The particular case of the Mediterranean cities highlights a significant deficit in precipitation in DJF and especially in JJA, and a smaller overestimation in MAM than over the rest of the country. The incoming solar radiation is overestimated for all seasons and climate conditions. As a result, CNRM-AROME tends to overestimate minimum temperature and under-estimate maximum temperature, with geographical and seasonal variations. The warm biases of TN are generally larger in summer due to the solar radiation overestimation, and the biases of TX are systematically smaller in spring in response to excess rainfall. Again, CNRM-AROME performs less satisfactorily for the Mediterranean cities that have complex geographical environment (coastline and relief). TN is there overestimated in all seasons, and the spatial variability of TX is poorly reproduced.

The main limitations of CNRM-AROME, highlighted by this local-scale evaluation, are in agreement with the findings of [START_REF] Lucas-Picher | Evaluation of the convection-permitting regional climate model CNRM-AROME41t1 over northwestern Europe[END_REF] for northwestern Europe. They found a generalized overestimation of incoming solar radiation, and they identified a deficit of total cloud cover as a major source of error in the simulation of radiation that could explain the warm summer biases. Concerning precipitation, the studies on CP-RCM generally indicate improved simulated daily and hourly rainfall rates with increasing horizontal resolution of climate models [START_REF] Reder | Performance evaluation of regional climate model simulations at different spatial and temporal scales over the complex orography area of the Alpine region[END_REF]. The overestimations found in the present study over most cities could be the result of overly active convection in CNRM-AROME, as suggested by [START_REF] Lucas-Picher | Evaluation of the convection-permitting regional climate model CNRM-AROME41t1 over northwestern Europe[END_REF]. Nonetheless, CNRM-AROME demonstrated good performances in multi-year seasonal biases of hourly precipitation over the Mediterranean cities, except in summer. In particular, previous works clearly showed added-value of the CP-RCM for the simulation of high precipitation events that affect the Mediterranean region in autumn [START_REF] Fumière | Extreme rainfall in Mediterranean France during the fall: added value of the CNRM-AROME Convection-Permitting Regional Climate Model[END_REF][START_REF] Reder | Performance evaluation of regional climate model simulations at different spatial and temporal scales over the complex orography area of the Alpine region[END_REF][START_REF] Caillaud | Modelling Mediterranean heavy precipitation events at climate scale: an object-oriented evaluation of the CNRM-AROME convectionpermitting regional climate model[END_REF].

Despite some biases in the simulation of precipitation and incoming solar radiation, CNRM-AROME realistically simulates the seasonal variability and minimum and maximum intensities of UHIN over most French cities of interest. The model is able to reproduce similar behavior between cities in the same climatic zones in response to local atmospheric forcing. UHIN is well simulated in Paris and Toulouse (semi-oceanic climate), in Dijon and Nancy (continental climate), and in Nîmes (Mediterranean climate). The most noticeable flaw of CNRM-AROME is the under-estimation of summertime UHIN in the southern coastal cities, that is mainly related to a poor representation of regional climatic conditions (insolation, precipitation, breeze circulation). As a result, the natural areas become too warm which reduces the temperature gradient with the city. The other limitation is related to the horizontal resolution which is still loose to accurately capture the UHI pattern of small cities. The UHI intensity is also under-estimated in this case because the urban parameters at the scale of a model mesh are smoothed. The performances are better for medium-sized and large cities. The urban canopy model TEB, implemented in the land-surface module of CNRM-AROME, then simulates fairly realistic urban temperatures with a clear added-value compared to the simple slab parameterization classically applied in RCMs. This finding has been demonstrated over Paris urban area by [START_REF] Lemonsu | What added value of CNRM-AROME convection-permitting regional climate model compared to CNRM-ALADIN regional climate model for urban climate studies? Evaluation over Paris area (France)[END_REF] by comparing CNRM-AROME with the CNRM-ALADIN RCM.

This study proposed an original evaluation of CNRM-AROME for urban studies, based on a multi-city analysis. The results demonstrate that a CP-RCM configuration, including a dedicated urban canopy model, can be relevant for studying weather conditions at the city scale in interaction with the local and regional climate. This opens interesting research perspectives for conducting impact studies for cities in a changing climate. In this context, work is underway to analyze the evolution of urban climate of an ensemble of European cities by the middle and end of the 21st century.

ACKNOWLEDGMENTS

We would like to Samuel Somot for his scientific advice and Antoinette Alias who played a key role in the development of CNRM-AROME and in performing several simulations of it.

APPENDIX Annex 1:

Figure 1 .

 1 Figure 1. Orography (in m) defined by the GMTED2010 (Global Multi-resolution Terrain Elevation Data) elevation model interpolated on the CNRM-AROME 2.5-km horizontal resolution grid (left). Simplified climatic zones based on Joly et al. (2010) (right).

Figure 2 .

 2 Figure 2. Elevation (in m, left) and major land use based on the ECOCLIMAP database, interpolated on the CNRM-AROME grid at 2.5-km horizontal resolution.

3. 1

 1 Figure 3 shows the 2000-2016 seasonal mean biases and RMSE of near-surface minimum and maximum daily temperature (TN and TX) simulated by CNRM-AROME in comparison to the ANASTASIA data.

Figure 3 .

 3 Figure 3. 2000-2016 seasonal mean bias (in °C) and RMSE (in °C) calculated between CNRM-AROME and ANASTASIA period for TN (left) and TX (right). The different biases are indicated using pie quarters, while RMSE is indicated according to the size of the quarters (see details top right of the left figure). The associated mean TN and TX observed are available in Annex 2.

Figure 4 .

 4 Figure 4. Nighttime UHI (in °C) observed (solid line) and simulated by CNRM-AROME (dashed line) over the period 2000-2016. For each city, UHIN are calculated with the TN time series recorded by the urban weather station and the spatial average of TN time series over rural area or with the TN time series at the CNRM-AROME grid points corresponding to the urban station and the spatial average of TN time series in rural area. Colors indicated climate zones: green for OCEANIC, orange for SEMI-OCEANIC, blue for CONTINENTAL and red for MEDITERRANEAN.

  The three continental cities (Dijon, Nancy, and Strasbourg) show comparable UHIN, with a weak range of intensities and nonetheless, a seasonal variability resulting in maximum intensities in JJA (+1 °C in June) and a minimum in DJF (around +0.4 °C). For the cities of Lille, Nantes, and Rennes (OCE), the oceanic influence limits annual temperature contrasts and gives rise to rainfall spread throughout the year. As a result, UHIs are low and have little seasonal variation (between +0.2 °C in DJF to +0.4/+0.6 °C in JJA). Although part of oceanic cities, Bordeaux displays greater values by 1 °C of UHIN throughout the year. Its UHIN climatology is more comparable to that of the cities of Toulouse (SOCE) and Nîmes (MED), with low seasonality and a range of variations between +1 and +1.5 °C, even if they are part of three different climatic zones. The two other Mediterranean cities of Montpellier and Nice show higher UHIN, especially Nice that has the strongest intensities for all seasons varying between +2.8 and +3.6 °C. Paris (SOCE) is the city that exhibits the most contrasted climatology of UHIN, with a maximum of +3 °C reached in April and a minimum of approximately +1.8 °C in January.

Figure 5 .

 5 Figure 5. 2000-2016 seasonal mean of nighttime UHI bias (in °C) calculated between CNRM-AROME and the observations. Seasons are presented using different pie quarters (see details on the top right corner of the figure).

Figure 6 .

 6 Figure 6. Mean annual cycle of TN (in °C) observed (solid line) and simulated by CNRM-AROME (dashed line) in urban station (in red) and rural environment (in green) in Bordeaux (left), Montpellier (middle) and Nice (right).

Figure 7 .

 7 Figure 7. Observed (left) and simulated (right) wind distributions for Montpellier in JJA at 18 UTC. Spoke indicated the frequency (in %) of winds blowing from and color provides details on speed (in m/s).

Figure 8 .

 8 Figure 8. Average annual cycle of EF (in %, green) simulated at the grid point corresponding to the Puéchabon station (solid line) and observed at the Puéchabon station (dashed line), precipitation (in mm/d, grey) and nighttime UHI (in °C, blue) over the period 2000-2018 in Montpellier.

Figure 8

 8 Figure8compares the observed and simulated monthly EF, averaged over 2000-2018. The simulated daily precipitation and UHIN are also displayed. The observed EF remains steady around 40% in winter, slightly increases until June to reach 45%, and then decreases during summer down to 30% in August, before increasing again until November. CNRM-AROME exhibits a larger range of monthly EF: between October and May, EF remains greater than 55%, and then decreases drastically from June to September with a minimum of 20% in August. This severe decrease in evapotranspiration in the rural environment is in line with the dry period indicated by the sharp decrease in rainfall simulated by CNRM-AROME (down to less than 0.5 mm per day in July) combined with a very high level of insolation. These conditions favor the soil drying, which could contribute to an excessive rise in rural temperature and result in a simultaneous decrease in UHIN (as noted in Figure8).

Figure 9 .

 9 Figure 9. Mean annual cycle of TN (in °C) observed (solid line) and simulated by CNRM-AROME (dashed line) in urban station (in red) and rural environment (in green) in Nantes (left) and Rennes (right).

Figure 10 .

 10 Figure 10. Left: major land use based on ECOCLIMAP database, interpolated on the CNRM-AROME grid at 2.5 km of horizontal resolution. Right: nighttime UHI (in °C) observed in the urban weather station (dark blue), the Météo-France network (light blue) and simulated by CNRM-AROME (blue) for Rennes.

Figure 11 .

 11 Figure 11. Nighttime UHI (in °C) maps over Dijon (left), Toulouse (center) and Paris (right) for JJA. Urban areas are indicated by hatching.

Figure 14 .

 14 Figure 14. Mean temperature, precipitation and incoming solar radiation normals plotted with ANASTASIA gridded product (blue, 2000-2016), COMEPHORE gridded product (grey, 2000-2018), and GEBA or Météo-France stations (orange), respectively. Annex 2:

City Location Alt. (m) Surf. (km²) Population (x1000) Urban station Environment Distance to the city center (km)

	NICE	43°42'1''N, 7°15'10''E	13	1479	550	Airport	8
	NÎMES	43°49'55''N, 4°20'34''E	76	790	257	Airfield	6
	PARIS	48°51'31''N, 2°20'49''E	31	814	7 075	Urban park	4
	TOULOUSE	43°36'02''N, 1°25'58''E	144	460	783	Airport	7
		2.3.2. General analysis of the CNRM-AROME for local climatic conditions
	BORDEAUX	44°51'49''N, 0°35'09''W	6	578	801	Airport	10
	LILLE	50°37'51''N, 3°02'48''E	23	614	1 174	Airport	8
	NANTES	47°14'17''N, 1°33'37''W	21	523	657	Airport	10
	RENNES	48°06'57''N, 1°41'18''W	24	705	451	Airport	7
	DIJON	47°19'54''N, 5°01'55''E	267	240	253	Airport	6
	NANCY	48°40'56''N, 6°11'29''E	205	142	257	Airport	6
	STRASBOURG	48°34'08''N, 7°45'43''E	136	337	500	Airport	11
	MONTPELLIER	43°36'36''N, 3°52'27''E	39	434	481	Airport	8

Table 2

 2 presents the 2000-2018 seasonal mean precipitation bias (in %) and RMSE of CNRM-AROME compared to COMEPHORE around the 12 French cities of interest.

Table 2 . Mean rainfall observed with COMEPHORE (in mm/day), seasonal mean bias (in %) and RMSE (in mm/day) calculated between the CNRM- AROME and COMEPHORE over the period 2000-2018. In bold, biases greater than ±20%.

 2

		DJF			MAM			JJA			SON
	CITY	Mean observed	Bias	RMSE	Mean observed	Bias	RMSE	Mean observed	Bias	RMSE	Mean observed	Bias	RMSE
		mm/day	%	mm/day	mm/day	%	mm/day	mm/day	%	mm/day	mm/day	%	mm/day
	BORDEAUX	2.70	+20.3	0.57	2.24	+31.0	0.70	1.74	-30.7	0.54	2.48	+7.6	0.23
	LILLE	2.16	+32.9	0.69	1.76	+54.7	0.96	2.30	+1.1	0.11	2.27	+11.8	0.27
	NANTES	2.79	+27.6	0.79	2.01	+35.5	0.73	1.59	-16.1	0.29	2.52	+12.5	0.33
	RENNES	2.61	+26.2	0.72	2.02	+43.6	0.89	1.77	-10.3	0.20	2.42	+14.4	0.36
	DIJON	2.32	+24.3	0.64	2.42	+35.1	0.87	2.51	-5.5	0.19	2.54	+0.58	0.10
	NANCY	2.42	+24.4	0.65	2.07	+46.1	0.96	2.34	+4.4	0.16	2.36	-1.4	0.13
	STRASBOURG	2.22	+63.5	1.46	2.16	+72.9	1.46	2.54	+22.3	0.57	2.27	+21.4	0.70
	MONTPELLIER	1.98	-11.3	0.33	2.10	+15.0	0.45	1.07	-44.0	0.49	3.42	+1.8	0.34
	NICE	2.64	-1.7	0.83	2.33	+18.8	1.33	1.26	-21.6	0.43	3.83	+0.1	1.01
	NÎMES	1.91	-12.1	0.28	2.08	+13.6	0.33	1.17	-45.5	0.56	3.67	-2.0	0.29
	PARIS	1.99	+28.7	0.59	1.84	+52.8	0.98	2.02	+2.3	0.16	1.89	+10.7	0.23
	TOULOUSE	1.87	+21.7	0.43	2.30	+37.9	0.89	1.72	-20.8	0.38	1.77	+14.8	0.28

Table 3 . Mean incoming solar radiation observed with GEBA (in W.m -²), seasonal mean bias (in %) and RMSE (in W/m²) calculated between the CNRM- AROME and the GEBA database over the period 2000-2018. In bold, biases greater than ±20 %.

 3

		DJF			MAM			JJA			SON
	CITY	Mean observed	Bias	RMSE	Mean observed	Bias	RMSE	Mean observed	Bias	RMSE	Mean observed	Bias	RMSE
		W.m-²	%	W.m-²	W.m-²	%	W.m-²	W.m-²	%	W.m-²	W.m-²	%	W.m-²
	BORDEAUX	64.7	+13.6	9.14	184.9	+9.8	18.82	241.6	+14.4	34.79	116.2	+15.0	18.00
	LILLE	40.4	+10.9	4.52	162.6	+5.6	10.35	206.7	+14.3	29.56	86.2	+13.0	12.23
	NANTES	54.2	+6.7	4.09	180.3	+3.9	7.50	234.4	+12.7	30.05	103.9	+12.2	13.49
	RENNES	50.5	+6.7	4.17	170.7	+6.9	11.91	223.9	+14.0	32.30	99.6	+10.9	12.02
	DIJON	49.5	+30.7	15.67	180.7	+12.5	23.79	236.7	+16.3	39.10	99.9	+21.8	22.67
	NANCY	43.1	+27.1	12.75	165.1	+14.8	24.79	225.8	+15.9	37.39	88.5	+24.7	24.22
	STRASBOURG	45.6	+35.5	16.67	168.6	+17.0	28.72	225.8	+16.7	32.20	90.5	+27.2	25.69
	MONTPELLIER	82.7	+12.0	10.25	212.9	+10.1	22.16	280.6	+8.9	25.11	132.1	+6.7	9.03
	NICE	81.2	+17.7	14.66	215.3	+11.2	24.44	287.5	+6.3	18.13	134.0	+10.6	14.40
	NÎMES	82.7	+11.5	9.98	212.9	+10.1	21.82	280.6	+9.1	25.90	132.1	+7.0	9.41
	PARIS	41.6	+22.8	10.53	168.9	+6.0	12.85	217.2	+15.0	35.56	91.7	+17.0	16.59
	TOULOUSE	70.0	+13.7	10.07	192.0	+9.3	20.90	244.6	+16.1	41.42	118.0	+18.4	22.18

Table 4 . Mean TN and TX observed in ANASTASIA (in °C).

 4

		DJF				MAM				JJA				SON
	CITY	Mean observed	TN	Mean observed	TX	Mean observed	TN	Mean observed	TX	Mean observed	TN	Mean observed	TX	Mean observed	TN	Mean observed	TX
		°C		°C		°C		°C		°C		°C		°C		°C
	BORDEAUX	2.8		10.8		7.3		18.5		14.3		26.8		9.2		19.7
	LILLE	1.9		7.1		5.7		14.8		12.8		22.7		8.2		15.6
	NANTES	3.1		9.7		6.6		16.4		13.3		24.5		8.9		17.9
	RENNES	2.9		9.2		6.0		15.7		12.5		23.5		8.5		17.1
	DIJON	-0.1		6.1		5.1		15.8		13.0		25.0		7.0		15.8

Funding

This work was supported by the French National Agency under the future investment program ANR-18-MPGA-0005.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

DECLARATIONS Ethical approval

Not applicable.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author's contributions

Yohanna Michau: Data collection, Data analysis, Methodology, Visualization, Writing (an original draft), Writingreview and editing.

Aude Lemonsu: Methodology, Writing-review and editing.

Philippe Lucas-Picher: Methodology, Writing-review and editing.

Cécile Caillaud:

Performing CNRM-AROME simulations, Writing-review and editing.

Declaration of interests

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐ The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Conflict of Interest


Sepideh Kalateh
email: sepideh.kalateh@uninova.pt

Sanaz Nikghadam Hojjati
email: sanaznik@uninova.pt

Luis Alberto Estrada-Jimenez

Terrin Pulikottil
email: tpulikottil@uninova.pt

Jose Barata

Open Innovation Association with Feeling Economy

Keywords: Open Innovation, Feeling Economy, Creativity, Soft Skills, Artificial Intelligence, Collaborative Networks, Human-Centered

Innovation and technology have always worked together to accelerate human evolution and society´s development. However, emerging technologies including Artificial Intelligence (AI), Collaborative Networks (CN), Innovation Platform, automation, etc., leapfrogged this process to a new level. Through AI, Machines perform tasks related to recognition and control better than human. On the other hand, in near future "thinking"-based skills will not grantee employment and wage creation, however, considering machine lack in automating human feeling, the feeling related skills which are "soft" aspects of job could define human tasks. Current review paper aims to study the association of Open Innovation (OI) Platform as a type of CNs with Feeling Economy as a future trend of emerging economy. In view of importance of collaborative and innovative network's role in smooth and sustainable transition from a thinkingbased economy to an emotion-based economy, this work provides a clear understanding of OI and Feeling economy concepts and their inline transition and requirements based on a systematic review.

Introduction

The increasing complexity and dynamism of markets, growing availability of private venture capital, number of knowledge workers and their mobility level and technology development has pushed business owners to accept the consequences of adapting a new paradigm which was opposed to their closed and private traditional research labs [START_REF] Nambisan | On open innovation, platforms, and entrepreneurship[END_REF][START_REF] Frosch | Strategies for manufacturing[END_REF].

Businesses that use high-tech AI-based solutions in their life cycle, get courage to open their doors to the flow of knowledge, innovations and ideas to survive and create competitive advantage. By accessing to the internal data of the business, AI empowered OI solutions provide better understanding of its business issues, weaknesses, and strengths. By gathering and analyzing external data and resources, they provide optimal solutions and possible opportunity for business challenges.

Despite the role of technology in improving business, its impact on jobs cannot be ignored. There are three perspectives to the impact of technology on employment rate:

-Doomsayer´s perspective considers technology as a factor to improve efficiency in human labour by tasks automation [START_REF] Pajarinen | Computerization threatens onethird of finnish and norwegian employment[END_REF]. -Optimist´s perspective, suggests that many types of labour may be replaced by technology, but the benefit and efficiency that achieve from technological empowerment outweigh transition costs [START_REF] Frase | Four futures: Life after capitalism[END_REF][START_REF] Avent | A world without work is coming-it could be utopia or it could be hell[END_REF]. -It indicates, by developing automated technologies, workers may require more social skills, as automating this type of skills is hard [START_REF] Deming | Skill requirements across firms and labor markets: Evidence from job postings for professionals[END_REF]. Unifying Perspective, suggests that multiple assets will accompany technological changes and create uncertainty about future works [START_REF] Autor | The skill content of recent technological change: An empirical exploration[END_REF] and technology directly will influence the demands for special skills [START_REF] Autor | The polarization of job opportunities in the us labor market: Implications for employment and earnings[END_REF][START_REF] Maccrory | Racing with and against the machine: Changes in occupational skill composition in an era of rapid technological advance[END_REF], therefore, it results in increasing demands in jobs, which typically require non-repetitive and soft skills and as a consequence rising salary in related jobs [START_REF] Tidd | Managing innovation: integrating technological, market and organizational change[END_REF].

Each of these different perspectives motivate changes in current economic trends and skills requirement for human resources to have a successful career in near future. However, accuracy of a forecast is highly depending on accurate understanding of past and present situations and most commonly by analysing the trends. Facing the need for future transformation processes worldwide regarding to changes on the job nature and emergent skills, the challenges for labour productivity, employment and mobility, the quality and new forms of work, and how technology can help to create this ambitious and human feelings centered environment, requires huge studies and research [START_REF] Goos | The impact of technological progress on labour markets: policy challenges[END_REF][START_REF] Brzeski | The robots are coming. Consequences of automation for the German labor market[END_REF][START_REF] Bessen | How computer automation affects occupations: Technology, jobs, and skills[END_REF].

The present systematic review paper aims to study the role of OI in association and accelerating the economy of feeling as a possible future trend for economy. The main novelty is that the paper assesses and validates the analysis outcomes and propositions of previous works regarding the relation between OI and feeling economy with a look at skills needs necessary for the new job content in emerging era. This will trace the baseline to understand, analyze and discuss the soft skill factors that can have a direct influence in the OI platform success and also the impact of OI in moving toward more feeling oriented working environment. At the end of this paper a set of recommendations will be elaborated to guide the further development of the work.

Methodology

Objectives and Research Questions

This section will provide a clear understanding of the research trends in relationship between soft skills and OI in the world of smart industry and digitization. The Systematic Review Methodology is used as a baseline to structure my work. Interested readers can have a detailed description of such methodology at [START_REF] Snyder | Literature review as a research methodology: An overview and guidelines[END_REF][START_REF] Xiao | Guidance on conducting a systematic literature review[END_REF].

A summary of the objectives of this research paper can be described in the following research questions:

1. Does OI based CNs has relationship with feeling economy trend? 2. What are the current trends and deviations in relation between soft skills and OI? 3. What will be the future trends for soft skills' necessity in OI based on paper review? Following section will describe the methodology used to fulfill the objectives of this paper to answer the questions.

Methodology

For the search and selection of the adequate articles, Scopus database was used. It is one of the most well-known and large academic article databases. Various terms were used recursively and combined to conduct literature review part. However, the most important and final terms are composed of keywords "Open Innovation" and "soft skill" and "Feeling Economy" without any further criteria on all fields.

Totally 172 papers have been suggested by the Scopus database which have been refined in different steps as are shown in Fig. 1 and reached to 71 relevant papers.

Open Innovation and Necessity of Soft Skills' Presence

In the past decades, innovation and creativity illustrated a critical role in the successful performance of organizations in the long and short term. It acts as an influential tactic for organizations to obtain competitive advantages by inventing new products, services and processes to underserved or unserved customers [START_REF] Huston | Connect and develop[END_REF].

Previous researches show that innovations and CNs create added value for involved partners by lowering the costs of services or existing products, improving their quality, introducing innovative products or services which there is enough demand in the target market, or delivering improved business models [17,[START_REF] Camarinha-Matos | Collaborative networks: A pillar of digital transformation[END_REF]. Furthermore, it has been observed that extreme innovations transform, destroy or create new markets by introducing new solutions to existing challenges. Therefore, researchers are interested in addressing innovation and innovation flows as a key factor on companies' financial success and markets economy [START_REF] Badir | Employee-level open innovation in emerging markets: linking internal, external, and managerial resources[END_REF][START_REF] Zheng | Inflation and growth: A non-monotonic relationship in an innovation-driven economy[END_REF]. It has been considered as an effective remedy for difficult and complicated global situations such as Covid-19 pandemic [START_REF] Chesbrough | To recover faster from covid-19, open up: Managerial implications from an open innovation perspective[END_REF][START_REF] S´a | Digitainability-digital competences postcovid-19 for a sustainable society[END_REF].

The emphasis on openness of knowledge and innovation flows within and outside the companies' boundaries has played a key role to the evolution of OI and generation of novel methods of collaboration networks [START_REF] Nambisan | On open innovation, platforms, and entrepreneurship[END_REF][START_REF] Nikghadam Hojjati | Computational Creativity to Design CyberPhysical Systems in Industry[END_REF]. OI indicates, companies demand innovation and knowledge flows whether inside or outside their boundaries to empower flow of innovation, while at the same time looking for external markets and partners to commercialize their internal knowledge Fig. 1: Systematic Review Methodology, from [START_REF] Snyder | Literature review as a research methodology: An overview and guidelines[END_REF][START_REF] Xiao | Guidance on conducting a systematic literature review[END_REF] assets, technologies and innovation [START_REF] Chesbrough | Open innovation: Researching a new paradigm[END_REF]. OI also results in an uneven growth in productivity and prosperity which has direct impact on economy [START_REF] Bogers | Open innovation: Research, practices, and policies[END_REF]. OI aids in a shift to produce greater openness for knowledge and innovation sharing. This process occurs from the inside to the outside of the company and vice versa [START_REF] Nambisan | On open innovation, platforms, and entrepreneurship[END_REF]. Wide-spread collaboration and communication networking are critical factors of this platform.

Based on reviewed papers [START_REF] Sopa | Hard skills versus soft skills: Which are more important for indonesian employees innovation capability[END_REF], effective performance in OI platform demands high efficiency of communication, leadership, collaboration, Critical thinking and Flexibility and Agility. Simultaneously, significant required soft skills in OI platform can be summarised as below:

-Collaboration -Leadership -Communication -Flexibility and Agility -Critical thinking Soft skills are essential to empower the smooth outflows and inflows among various sectors inside and outside the companies' boundaries [START_REF] Lazaro-Mojica | Review paper on the future of the food sector through education, capacity building, knowledge translation and open innovation[END_REF][START_REF] Al Amri | Towards sustainable i4.0: Key skill areas for project managers in gcc construction industry[END_REF][START_REF] Schneider | Digital transformation: What we have learned (thus far) and what is next[END_REF][START_REF] Wehrle | The impact of digitalization on the future of the psm function managing purchasing and innovation in new product developmentevidence from a delphi study[END_REF]. Employees' soft skills have become critical factor in evaluating their value to a company [START_REF] Florek-Paszkowska | Business innovation and critical success factors in the era of digital transformation and turbulent times[END_REF]. Companies are looking for new training and recruitment methods to increase their employees' soft skills alongside the hard skills to guaranty their successful performance in OI [START_REF] Bello-Pintado | Consequences of open innovation: effects on skilldriven recruitment[END_REF][START_REF] Oh | The competence of project team members and success factors with open innovation[END_REF]. In next section we will dig more in the added value of soft skills and the new era of economy based on feeling.

Economy Based on Feeling

Contemporary industry is facing dramatic changes. It is experiencing different way of producing products, providing services, business processes, and chain management [START_REF] Pulikottil | Predictive manufacturing: Enabling technologies, frameworks and applications[END_REF][START_REF] Wang | A problem solving oriented intelligent tutoring system to improve students' acquisition of basic computer skills[END_REF] alongside the changes in required skills and training for the workforce [START_REF] Dhanpat | Industry 4.0: The role of human resource professionals[END_REF]. Based on world economic world 50 percent of employees will need reskilling by 2025, 40 percent of skills are expected to change in the next 5 year [37].

New digital technologies such as AI, machine learning and internet of things are affecting the nature of work spaces and jobs [START_REF] Borges | The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions[END_REF]. Related studies have proved the important role of tasks automation on workforce and companies across Europe showing that automation will affect 35 percent of employment in Finland [START_REF] Pajarinen | Computerization threatens onethird of finnish and norwegian employment[END_REF] 59 percent of employment in Germany [START_REF] Brzeski | The robots are coming. Consequences of automation for the German labor market[END_REF] in near future. New jobs are likely to be focused on the non-routine and emotive tasks which require higher-order of soft skills which are less probable to be automated in near future [START_REF] Ra | The rise of technology and impact on skills[END_REF][START_REF] Kalateh | Feeling smart industry[END_REF]. Furthermore, some experimental evidence demonstrates that, even though availability of information is crucial to make an analytical decision, people tend to make the decision based on their feelings [START_REF] Ra | The rise of technology and impact on skills[END_REF][START_REF] Bandelj | Emotions in economic action and interaction[END_REF][START_REF] Gruber | States of curiosity modulate hippocampusdependent learning via the dopaminergic circuit[END_REF].

This also leads to higher demand for jobs that are more difficult to automate and need high quality of soft skills like communication, collaboration, empathy and critical thinking that are essential elements of toady's successful business nature [START_REF] Heffernan | They say they want a revolution[END_REF][START_REF] Margherita | The role of human resource practices for the development of operator 4.0 in industry 4.0 organisations: a literature review and a research agenda[END_REF]37]. Therefore, the most important skills of the new work force are not just technical skills anymore, but also emotive and interpersonal ones, which highlight the need of an economy based on feelings [START_REF] Miranda | Open innovation laboratories as enabling resources to reach the vision of education 4[END_REF]. This is an economy in which the emotive tasks are the essential tasks and employment with higher soft skills are more likely to receive more wages comparing the total employment and wages attributable to thinking or mechanical tasks [START_REF] Huang | The feeling economy: Managing in the next generation of artificial intelligence (ai)[END_REF][START_REF] Kalateh | Feeling smart industry[END_REF].

Many researches show that Industry has become more feeling oriented in the past years and it is a sustainable process. The Fig 2 represents the researches within 2012 till 2022 years from Scopus for keywords "soft skill" or "emotive skill". As it illustrates, the "Business, Management and Accounting" area represents the second highest number of publications by field, which shows the significant role of soft skills in this area. Openness of innovation and knowledge flows happens when the required tools with the necessary skills are presented, in the next section we will discuss feeling economy and OI association and convergence.

Results and Discussion

In recent years, OI research has advanced on different aspects. The studies that focus on multiple perspective of influential factors on OI provide valuable insights on a whole range of essential required factors that shape OI processes flow which result in a platform of innovations in collaborative and interactive network [START_REF] West | Open innovation: The next decade[END_REF].

Earlier we asked about Feeling economy and OI association and discussed their significant features. The Fig. 3 illustrates the OI and feeling economy inline interactions. OI needs to fulfill essential factors to accelerate the outflow and inflow of innovation. One of the required factors is soft skills [START_REF] Miranda | Open innovation laboratories as enabling resources to reach the vision of education 4[END_REF][START_REF] Gunawan | Peran studi kelayakan bisnis dalam peningkatan umkm (studi kasus umkm di kabupaten kudus)[END_REF]. At the same time smart industry tends to delegate more thinking and repetitive tasks to new technologies which provide even more apace for human workforce to focus on what is their advantages, soft skills. Emerges of soft skills, create an environment which people are well-trained or eager to educate in emotive skills, and they can communicate and collaborate in productive way in CNs.

OI by providing an innovative environment alongside the open exchange of knowledge beyond company boundaries and proactive networking provide a high potential environment for human soft skills to be expressed and trained. OI association with feeling economy provides a proactive platform for industries successful growth and establishment.

New era is emerging, era of feeling economy and the recent advancements in smart industry along the companies' needs to establish a concrete network whether inside or outside their boundaries in order to be successful in globalization markets [START_REF] Rios | Identifying critical 21stcentury skills for workplace success: A content analysis of job advertisements[END_REF]. Although, innovative technologies and tools are making it easier and more convenient, this cannot be achieved without the presence of soft skills [START_REF] Lavrynenko | Managing skills for open innovation: the case of biotechnology[END_REF].

Conclusion

Current research has critically analyzed association of OI with Feeling Economy and importance of soft skills in near future and in future industrial revolution (Industry 5.0). However, emerging technologies like AI and machine learning may help us in accelerating and improving thinking tasks, but they are still straggling support human societies and work place in emotive and creative related tasks as good as human beings which lead us to the new economical trend which is based on human feeling.

This study highlighted that successful launch and maintenance of innovative networking platform like OI is in high association with soft skills. By showing the future trends, it illustrates the path, required skills and education for innovative CNs. This study showed the trends toward more emotive networking by the usage of smart industry tools therefore it enables the companies to plan with clearer vision to train their workforce, change their business vision and recruitment strategies and be ready to tackle the changes of emerging era of Feeling Innovative CNs to perform successfully in near future.

The results of this research put light on a deep research gap which could be addressed in future studies, including, developing knowledge, tools, methodologies and technologies which future workforce could apply to develop their skills and position in Feeling economy-based societies and Industry 5.0. Also, according to the finding of this study, there is a high expectation that future innovative ecosystems and business developments will be based on CNs particularly OI, which lead to the conclusion that current and future businesses require more attention in this type of platforms and networks to guarantee their business sustainability alongside the feeling skills which are the foundation of OIs ecosystem. However, there should be more in-depth study regarding the characteristic of feeling oriented working process and Feeling oriented innovation process to provide better understanding of the emerging new era of feeling and openness.

It is important to have a better understanding regarding the different aspects of CNs and OI, the skills required for working in such an environment, technologies that could help us to facilitate our communication such as affective computing or computational creativity, methodologies and assessment tools, regulation, ethics and data privacy, etc. Due to the reality that women traditionally considered as humans which has better soft skills, a question will be raised based on this study is, how gender equality could play a role in an economy which is based on feeling, and what will be association between these two.

Fig. 2 .

 2 Fig.2. Research Trends toward the importance of "soft skill" in "Business" area.

Fig. 3 .

 3 Fig.3. OI and Feeling economy association.

Acknowledgement.

This work was supported in part by the FCT/MCTES (UNINOVA-CTS funding UID/EEA/00066/2019) and the FCT/MCTES project CESME -Collaborative and Evolvable Smart Manufacturing Ecosystem, funding PTDC/EEIAUT/32410/2017.