Florent Beucher

Fleur Couvreux

Dominique Bouniol

Ghislain Faure

Florence Favot

Thibaut Dauhut

Alex Ayet

Process-oriented evaluation of the oversea AROME configuration: Focus on the representation of cloud organisation

Keywords:

published or not. The documents may come

1 spin-up phase and only use the 6h to 30h forecast ranges. AROME-OM system is basically a 2 downscale, without any additional data assimilation, of the deterministic global model Integrated 3 Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts 4 (ECMWF). The model is forced on its lateral boundaries at an hourly frequency [START_REF] Faure | Operational Implementation of the AROME Model in 33 the Tropics: Multiscale Validation of Rainfall Forecasts[END_REF]. [START_REF] Imaoka | Instrument performance 6 and calibration of AMSR-E and AMSR2[END_REF] It uses 90 vertical levels with 41 levels below 700 hPa. The continental surface initial fields are 6 taken from the global model ARPEGE, which uses the same surface model as AROME-France, 7 known as SURFEX [START_REF] Masson | The SURFEXv7.2 land and ocean surface platform for coupled 24 or offline simulation of earth surface variables and fluxes[END_REF]). The SST are initialized by the MERCATOR global model 8 PSY4 at 1/12 degree [START_REF] Lellouche | Recent updates to the Copernicus Marine Service global 18 ocean monitoring and forecasting real-time 1/128 high-resolution system[END_REF] and since 2017 AROME-OM has been coupled with a 9 1D vertical Mixed-Layer Ocean Model (CMO) [START_REF] Brossier | Two-way one-dimensional high-15 resolution air-sea coupled modelling applied to Mediterranean heavy rain events[END_REF]. AROME-OM has no 10 specific tuning of its physical parameterizations compared to the AROME configuration (see 11 details in [START_REF] Seity | 22 'The AROME-France Convective-Scale Operational Model[END_REF]. Note that at a 2.5 km grid spacing, the deep convection is permitted but 12 the shallow convection is parameterized by a mass-flux scheme based on a stationary bulk updraft 13 [START_REF] Pergaud | A Parameterization of Dry Thermals and 46 Shallow Cumuli for Mesoscale Numerical Weather Prediction[END_REF]. [START_REF] Brossier | Two-way one-dimensional high-15 resolution air-sea coupled modelling applied to Mediterranean heavy rain events[END_REF] The assessment of AROME-OM is carried out over the French Caribbean domain (9.7-15 22.9°N, 75.3°W-51.7°W, 1200 x 2000 km²) (Fig. 1a). The evaluation focuses on the south-eastern 16 part of this domain (Fig. 1a, black dashed box, 500 × 1000 km²) where the EUREC 4 A campaign 17 mainly took place. An additional forecasting setup of AROME-OM at a 1.3 km horizontal grid 18 spacing, the near-future operational configuration, was also performed specifically during 19 EUREC 4 A. This simulation was regridded at a 2.5 km resolution to ensure a fair comparison with 20 the operational configuration. Evaluation of this future configuration is focused in section 4.3. 21 22 2.2 Evaluation procedure 23 24 The evaluation of AROME-OM at a 2.5 km resolution spans across three winter seasons 25 (January-February) from 2018 to 2020 which ensures that a wide variety of large-scale 26 environments isare sampled. This evaluation is carried out against data collected at both the 27 Grantley Adams Airport (59.49°E, 13.07°N) and the BCO platform [START_REF] Stevens | The Barbados Cloud Observatory: 30 Anchoring Investigations of Clouds and Circulation on the Edge of the ITCZ[END_REF]) (Fig. 1b) 28 which is representative of a downwind trade regime where broken shallow cumuli prevail 29 [START_REF] Medeiros | Clouds at Barbados are representative of clouds across the trade 29 wind regions in observations and climate models[END_REF]. The dropsondes [START_REF] George | JOANNE : Joint 37 dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale 38 Environments[END_REF] that have been released along a 30 circle by the HALO aircraft during the EUREC 4 A campaign are also used for this evaluation. [START_REF] Narenpitak | From sugar to flowers: A 32 transition of shallow cumulus organization during ATOMIC[END_REF] Datasets used for the evaluation procedure are summarized in Table1. 32 33 2. [START_REF] Hourdin | Parameterization of the dry convective boundary layer 4 based on a mass flux representation of thermals[END_REF] The BCO platform 34 35 In addition to standard surface meteorological measurements at 2 m above ground (wind, 36 temperature, humidity), the Max Planck Institute for Meteorology has installed at the BCO supersite 37 (https://barbados.mpimet.mpg.de) various advanced vertically-pointing remote-sensing devices. 38 Vertical profiles of hydrometeors (cloud and rain) are derived using the CORAL Ka-Band cloud 39 radar with a profile every 10 s and a vertical resolution of 30 m up to 18 km. The high sensitivity of 40 -55 dBZ at 5-km height for a 10-s averaging time, below which radar reflectivity is filtered, allows 41 to detect a wide spectrum of clouds in the entire troposphere [START_REF] Görsdorf | A 35-GHz Polarimetric Doppler Radar for Long-Term Observations of 43 Cloud Parameters-Description of System and Data Processing[END_REF]. For a fair 42 comparison with AROME-OM, the cloud radar profile (605 regular levels) is regridded on the 43 model vertical grid (90 levels). A roughly 30-min temporal averaging of the cloud radar profiles is 44 applied in order to match the horizontal scale resolved by the model [START_REF] Hogan | Comparison of ECMWF winter-season cloud 49 fraction with radar-derived values[END_REF], Bouniol et 45 al. 2010). This averaging time is computed by estimating the required time for an air parcel to cross 46 the length of the effective horizontal resolution of the model advected by the horizontal wind. The 47 effective resolution is impacted by the implicit diffusion of the semi-implicit semi-lagrangian 48 scheme used in AROME [START_REF] Malardel | An Alternative Cell-Averaged Departure Point Reconstruction 21 for Pointwise Semi-Lagrangian Transport Schemes[END_REF] and has been estimated as about nine times its 49 native grid spacing [START_REF] Ricard | Kinetic energy spectra 5 characteristics of two convection-permitting limited-area models AROME and Meso-NH[END_REF]. To suppress echoes of rain below cloud base, cloud base 50 height and rain flag (rain-rate > 0.05 mm/hr) are derived using ceilometer and micro-rain radar (see Two different instruments are used for precipitable water (PW): a ground-based microwave 4 radiometer was installed in 2018 [START_REF] Schnitt | Potential of Dual-Frequency Radar and 14 Microwave Radiometer Synergy for Water Vapor Profiling in the Cloudy Trade Wind 15 Environment[END_REF], followed by a Ground-based Global 5 Navigation Satellite System (GNSS) BCON station in 2020 [START_REF] Bock | Integrated water vapour observations in the Caribbean arc from a network of ground-12 based GNSS receivers during EUREC 4 A[END_REF]. These datasets are 6 valuable for an evaluation of AROME-OM since they are not assimilated by its forcing IFS model, 7 with the caveat that the BCON GNSS shows a dry bias of 1.6 mm compared to the BCO sondes 8 [START_REF] Bock | Integrated water vapour observations in the Caribbean arc from a network of ground-12 based GNSS receivers during EUREC 4 A[END_REF]). 9 10 2.4 Grantley Adams airport soundings 11 12 Within the Caribbean domain, five twice-daily (1100 UTC and 2300 UTC) radiosoundings 13 launched in Santo Domingo, San Juan, Le Raizet (Guadeloupe), Grantley Adams Airport 14 (Barbados), Trinidad and Tobago (Fig. 1a, blue stars) are assimilated into the IFS 4D system. 15 Within the domain targeted in this study, only the sounding of Barbados Airport (~ 5000 vertical 16 levels) remains for the assessment of the vertical thermodynamical profiles of AROME-OM. The 17 airport is located at an altitude of 52 metres in the southern part of the island, a few hundreds metres 18 away from the sea. It is located on the lee side of the trade winds which may differ slightly from 19 "pure" maritime conditions. The nearest grid point of the airport in AROME-OM is located 900 m 20 southwards at an altitude of 32 metres allowing an evaluation with a good accuracy. During 21 EUREC 4 A, soundings were also launched at the BCO but the period is not long enough for the 22 evaluation of AROME-OM thermodynamics. 23 24 2.5 EUREC 4 A dropsondes 25 26

During EUREC 4 A, additional vertical profiles are provided by the release of about twelve 27 dropsondes [START_REF] George | JOANNE : Joint 37 dropsonde Observations of the Atmosphere in tropical North atlaNtic meso-scale 38 Environments[END_REF] along each HALO circle. The HALO circle path (Fig. 1a, blue 28 circle) extends between 12.3°N and 14.3°N [START_REF] Konow | EUREC 4 A's HALO[END_REF], centred approximatively 1° 29 westwards of the BCO. To mimic the observations, the AROME-OM outputs have been averaged 30 along the circle. The evaluation of the parameters near the surface is carried out at 1000 hPa since 31 data from dropsondes are sometimes not available below this level. The dropsondes have also been 32 used to compute the vertical velocity from the divergence of the horizontal wind over the entire 33 circle (Bony and Stevens, 2019). For its evaluation, the simulated vertical velocity has also been 34 averaged over the entire circle in AROME-OM to ensure a fair comparison. 35 36 2.6 Satellite observations 37 38 In order to document the spatial variability, the dataset is complemented by observations 39 provided by spaceborne sensors, in particular from the visible channel 2 of the Advanced Baseline 40 Imager onboard the geostationary satellite GOES16. The satellite measures reflectance at a 0.5 km x 41 0.5 km resolution and a 10 minute temporal frequency. The combination of this reflectance and of a 42 2-km x 2-km cloud mask from the Nowcasting Satellite Application Facilities (SAFNWC) allows 43 us to construct a gridded field of cloud fraction at a 2-km x 2-km resolution. This gridded field 44 accounts for partial cloudiness inside a grid point instead of using a binary cloudiness flag. The 45 evaluation of the low and mid cloud cover (up to 450 hPa) of AROME-OM and IFS is carried out 46 against this reconstructed field. 47

To estimate PW, Advanced Microwave Scanning Radiometer 2 (AMSR2) measurements 48 [START_REF] Imaoka | Instrument performance 6 and calibration of AMSR-E and AMSR2[END_REF] onboard Global Change Observation Mission-Water (GCOM-W1) satellite at a spatial 49 resolution of about 25 km x 25 km are also used. PW is retrieved from four AMSR2 channels Figure 2 compares the wintertime composite soundings of 2300 UTC over the three 10 considered winters (January-February 2018-2020) with the vertical profiles of AROME-OM and of 11 the IFS model. The vertical profiles at 1100 UTC are not shown since they exhibit roughly the same 12 biases. Both potential temperature and specific humidity profiles show too deep a sub cloud layer 13 (SCL) in AROME-OM with a top located too high (~920 hPa) compared to the observations (~ 14 940/950 hPa). This shortcoming also partly explains why AROME-OM is too dry (-1g/kg) in the 15 subcloud layer and too cold in the cloud layer (-0.5 K between 930-800 hPa). In contrast to 16 AROME-OM, the SCL is too thin in IFS compared to the observations, suggesting that the forcing 17 model does not control the thickness of the SCL in AROME-OM. It is hypothesized rather that the 18 shallow convection scheme [START_REF] Pergaud | A Parameterization of Dry Thermals and 46 Shallow Cumuli for Mesoscale Numerical Weather Prediction[END_REF] of AROME has to be tuned in order to reduce the 19 strength of the updraft or to adjust the entrainment/detrainment rates at the top of the SCL. The 20 overestimation of the wind speed (+1 to +2 m/s) in AROME-OM up to the trade wind inversion 21 (~800 hPa) could be partly driven by IFS as it roughly shows identical biases. This overestimation 22 of the trade winds in IFS has been recently confirmed by Savazzi et al. (2022) by using sondes that 23 have not been assimilated by IFS. Note that the 95% confidence interval of each environmental 24 profile (OBS and AROME) is rather weak which underlines the low intraseasonal variability of the 25 large-scale environment in which subtropical shallow convection form. Evaluation at the other 26 sounding sites in the Caribbean domain indicate similar biases. A simulation of AROME-OM 27 recently run over a vast domain from Barbados to Cape Verde during the period of EUREC 4 A 28 shows similar biases suggesting that the thermodynamics biases are rather linked to AROME 29 physical parameterizations than associated with the biases of the forcing model. In the following 30 sections, besides these existent biases, albeit weak, we analyse the ability of AROME-OM to 31 represent the shallow convection in terms of vertical and horizontal distributions. 32 33 3.2 Evaluation of the cloud fraction 34 35 Figure 3 shows an evaluation that spans the three considered winters (January-February 36 2018-2020) of the vertical distribution of the cloud fraction and of its occurrence frequency at the 37 BCO point. For a fair comparison with the AROME-OM hourly snapshots, cloud radar profiles are 38 averaged in time and vertically regridded (details in section 2.3). The AROME-OM vertical profile 39 of cloud occurrence (Fig. 3a) fits well the observed one with a peak of frequency of about 60-70% 40 near the cloud base, 900 hPa for AROME-OM and 920 hPa for the radar. As discussed in the 41 previous section, too thick and too cold a SCL explains why the cloud base is located too high in 42 the model. The radar profile shows a secondary peak of occurrence frequency at about 820 hPa 43 which is not simulated by the model (Fig. 3a). In contrast, when focusing on situations of cloud 44 occurrence (Fig. 3b), the cloud fraction of the secondary peak is well represented by AROME-OM 45 with values reaching 0.06. It demonstrates the ability of AROME-OM to mimic stratiform-like 46 clouds, although the occurrence of such clouds is significantly underestimated (Fig. 3a). The 47 temporal evolution of the cloud depth during EUREC 4 A shows that AROME-OM (Fig. 4a) 48 constructs two distinct modes of shallow convection: i) a first one, the most frequent, made of very 49 shallow convection (VSC) with a peak of cloud fraction of about 0.15 near the cloud base and ii) a 1 second one made of more vertically developed shallow convection (VDSC) with saturated cloud 2 fraction (magenta dot) near the cloud top. This latter feature illustrates that the updraft associated 3 with the cloud can cover the whole gridpoint. Indeed at a 2.5 km resolution the shallow convection, 4 in particular the stratiform one, is partially resolved [START_REF] Honnert | Representation of the grey zone of turbulence in the atmospheric boundary layer[END_REF]. As displayed in Figure 5 4b, the VSC mode is well observed in the cloud radar with a peak of occurrence of the maximum of 6 cloud fraction located near the cloud base, although values are slightly stronger (0.2-0.3) than in 7 AROME-OM. The VDSC mode exhibits divergent characteristics from AROME-OM with clouds 8 more vertically developed, the maximum of cloud fraction being instead located a few hundred 9 metres below the cloud top with a much more variable magnitude, from 0.3 to 1. For instance, from 10 22nd to 24th January 2020, the cloud top can exceed 4500 m in the observations and drops to 3000 11 m in AROME-OM. The distribution of the height of the base and the top of the cloud over the three 12 winter seasons (Fig. 5) confirms that AROME-OM shows a better ability to simulate the base than 13 the depth of the cloud. When small cloud fraction (<0.1) is filtered (Fig. 5b, right), the second peak 14 in the distribution of the cloud-top heights is well captured by AROME-OM. Overall, we may argue 15 that in spite of biases in the SCL, AROME-OM is able to simulate the double peak structure of the 16 shallow cumulus, with the caveat that the occurrence of the second peak is underestimated and 17 associated with an excessive cloud fraction when present. 18 [START_REF] Schulz | C 3 ONTEXT: A Common Consensus on Convective OrgaNizaTion during the 20 EUREC 4 A eXperimenT[END_REF]

Dependence of the cloud fraction on the large-scale environment 20 21

To gain further insight into how the large-scale environment affects trade wind cloudiness, 22 this section investigates the cloud fraction sensitivity to key controlling parameters such as 2m-23 temperature (t2m), 2m-zonal wind speed (u2m), and PW. Figure 6 shows the mean profile of cloud 24 fraction associated with the 20 % lowest (resp. highest) parameter values as well as for 60% of 25 intermediate parameter values. These calculations are carried out only for common dates between 26 observations and AROME-OM in order to characterize similar environments. The spread between 27 the 20% lowest and 20% highest of each parameter is rather weak (see values in the figure legends), 28 highlighting the low variability of the environment over this maritime subtropical domain. 60 % of 29 values are within 1 K for the t2m, 3 m/s for u2m, and 9 mm for the PW. 30

The large cloud fractions near 800 hPa (Fig. 6 upper panel) are associated with cold t2m and 31 high PW in observations and AROME-OM but are not really sensitive to the surface wind speed. 32 To better investigate the role played by the large-scale environment, the high-frequency variability 33 of t2m, u2m and PW has been removed by a low-pass filter of 48h (Fig. 6 lower panel). The cloud 34 base in the observations is particularly sensitive to synoptic fluctuations in u2m and PW (Fig. 6e-f). 35 An increase of u2m leads to a moistening in the subcloud layer and an increase of cloudiness near 36 the level of free condensation. The failure of the model in reproducing this sensitivity could not be 37 linked with the biases identified in the SCL since near the surface, wind speed roughly fits the 38 observations (Fig. 7). More in-depth studies are necessary to disentangle the implied physical 39 mechanisms but mesoscale circulations associated with more vertically developed cumulus or 40 strength of the surface moisture flux could be misrepresented at a 2.5 km grid spacing. It is worth 41 mentioning that at the strongest u2m the model shows rather a good ability to reproduce the 42 deepening of the cloud above 800 hPa (dotted lines, Fig. 6b,e), which confirms the findings of 43 Nuijens and Stevens (2012). 44

The second peak of cloudiness is the most influenced by large-scale environments, with 45 enhanced cloudiness on the days that are the coolest (Fig. 6d). This second peak is also influenced 46 by fluctuations of large-scale PW (Fig. 6f), suggesting that a drier environment favours stronger 47 trade wind inversion. In contrast, on the days that are the hottest, the cloudiness near 800 hPa 48 decrease. This is consistent with the findings of [START_REF] Schulz | Characterization and Evolution of Organized Shallow 17 Convection in the Downstream North Atlantic Trades[END_REF], showing that stratiform-like 49 structure rather originate from mid-latitudes (low t2m) whilst variable vertically extensive structure 50 are consistent with a more tropical influence (high t2m) (their Fig. 6 and10). Note that when PW is

F o r P e e r R e v i e w
1 not filtered (Fig. 6c), PW and cloud depth are strongly correlated since PW corresponds to the local 2 footprint of the clouds. [START_REF] Hourdin | Parameterization of the dry convective boundary layer 4 based on a mass flux representation of thermals[END_REF] Overall, it may be argued that the sensitivity of the large-scale environment is captured more 4 by the model at the cloud-top height than at the cloud base, suggesting that the subtle relationship 5 between clouds and environment are better represented at the level of the trade wind inversion than 6 in the subcloud layer or near the surface. 7 8 3.4 Evaluation of the environment variability on a daily to interannual scale 9 10 In the previous section, PW, u2m and t2m have been identified as large-scale parameters 11 that greatly control the cloud fraction of the shallow convection. To gain further insight into how 12 these parameters interplay, Figure 7 exhibits their 24h averaged time-evolution during three winter 13 seasons for both observations and AROME-OM at the BCO grid point. The evaluation of the 14 precipitation is carried out against precipitation forecasts averaged over a 0.25° square ahead of 15 BCO (Fig. 1b, pink square), which is equivalent to the effective resolution of the clouds. 16 Precipitation is accumulated over a 0600-0600 UTC period. 17 AROME-OM captures fairly well the interannual variability of the environment at the BCO, 18 with a relatively wet, hot and windy season in 2018 followed by a relatively dry, cool and calm 19 season in 2019. The intraseasonal variability, with successive dry and wet events, is also well 20 reproduced. The day-to-day variability of PW is consistent with the estimates of the microwave 21 radiometer (HATPRO) and of the GNSS retrievals, except for the strongest events which are 22 underestimated down to -5 mm. Bock 2020) could be related with this underestimation of 25 the peak of PW. In the absence of a strong peak of PW, as in 2019, the PW is in contrast 26 overestimated by AROME-OM by about 1 mm. The surface wind speed is also in good agreement 27 with the observations, except during the period of slackening of the winds (e.g. 15th-17th February 28 2019) where the overestimation in the model reaches 2 m/s. This bias could be partly accounted for 29 by the difference of land surface between the model and the BCO platform since the grid point in 30 AROME-OM is located a few hundreds metres away from the island. Although obviously less 31 pronounced near the surface, the overestimation of the wind is consistent with the bias observed in 32 radiosoundings (Fig. 2) at Grantley Adams Airport. The bias in surface temperature is not 33 significant (~-0.2 K), except during marked rainy events (e.g. 23th January 2020) with a warm bias 34 (~+1K) which may be related to the land surface difference between the grid point of AROME-OM 35 and the BCO.

36

It is not obvious to disentangle the interplay between PW, u2m, t2m and precipitation. The 37 relative dry season in 2019, dominated by low PW and scarce precipitation, appears to be correlated 38 with weak surface wind speed, which corroborates the literature [START_REF] Nuijens | The influence of wind speed on shallow marine cumulus 37 convection[END_REF]. In 39 2018 and 2020, except during the periods of sudden stilling in the trade wind regime (e.g. 1st-3th 40 February 2018), this relationship weakens. Indeed, the period of strengthening of the wind (e.g. 6th-41 9th February 2020) is not necessarily associated with a moister environment (PW~32mm). The 42 slow decrease of PW across the season 2018 is paired with that of t2m. However, this correlation is 43 not evident the two following years. The question of the link between precipitation occurrence and 44 PW was raised by [START_REF] Neelin | The transition to strong convection[END_REF]. They found that precipitation may be triggered above a 45 critical value of PW which is driven by the mean layer-integrated tropospheric temperature. This 46 threshold is around 36 mm over the Sahel in summer (Parker, 2017, their Fig. 3.5) whereas it drops 47 to about 25-30 mm over the subtropical ocean in winter which explains the high occurrence of rainy 48 days since this threshold is often exceeded. 49

This discussion shows how it is difficult to shed light on the interplay of parameters that 50 control cloud fraction. Nevertheless, the good ability of the model to reproduce their temporal In this section, we investigate the ability of AROME-OM to reproduce the four main 8 mesoscale cloud spatial organisations [START_REF] Stevens | 33 Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds[END_REF] and the environment in which they 9 form, with a particular focus on four different periods selected during the EUREC 4 A campaign. 10 11 4.1 Identification of four distinct large-scale environments during EUREC 4 A 12 13 Figure 8 shows the AROME-OM longitudinal Hovmöüller diagrams for the EUREC 4 A field 14 campaign (16 th January -16 th February 2020) averaged on the latitude band of the HALO circle for 15 various parameters which are likely to control the trade wind cumuli patterns: surface wind speed, 16 large-scale vertical velocity in the mid-troposphere (ω500-600 hPa), PW and estimated inversion 17 strength (EIS, Wood and Bretherton, 2006), defined as EIS = LTS -Γ 850(z 700 -LCL ) where Γ 18 850 is the moist-adiabatic potential temperature gradient at 850 hPa, z 700 is the height of the 700 19 hPa level, and LCL is the height of the lifting condensation level. In addition, the 6h accumulated 20 precipitation is shown. Focusing on the longitude of the HALO circle (in between the two vertical 21 dashed lines), several periods showing a distinct environment can be identified: 22 i) From 22 thnd to 25 th January, the air mass is under the influence of an extra-tropical 23 intrusion in a form of a trailing cold front, associated with both a shear line in the trade winds (Fig. 24 8d, see the horizontal dashed-dotted line) and an increase of PW (Fig. 8a, see the tongue-like 25 structure). [START_REF] Schulz | Characterization and Evolution of Organized Shallow 17 Convection in the Downstream North Atlantic Trades[END_REF] and [START_REF] Aemisegger | How Rossby wave breaking modulates the water cycle in the North 5 Atlantic trade wind region[END_REF] suggested that the Fish pattern 26 frequently arises in association with this synoptic environment. The concomitant decrease of the 27 inversion strength (Fig. 8b) favours the deepening of the shallow cumulus leading to a significant 28 precipitation event (Fig. 8c). The large-scale environment of this period is called E1. [START_REF] Dauhut | The Flower trade-wind clouds are shallow mesoscale 31 convective systems[END_REF] ii) This disturbed period is followed for about 4 days (27 th -30 th January) by fair weather with 30 very shallow cumulus (see the reports from the HALO https://observations.ipsl.fr/aeris/eurec4a/#/). 31 After the passage of the cold front, the depth of the trade wind layer decreases with calm winds at 32 the surface and above, at 700 hPa, a regime of light westerlies occurs. A decrease in wind speed 33 paired with a drop in surface temperature lead to a reduced latent heat flux and a relative decrease 34 of PW (Fig. 7c, see 27 th January). The joint effects of weak subsiding motion in mid-troposphere 35 and the absence of strong inversion trades (+1 to +3 K), which prevents the spreading of the cloud 36 tops, favours the occurrence of a field of small 'pop corn' cumuli or even a regime of suppressed 37 clouds. This environment associated with a 'pause' in the trade wind regime is called E2. 38 iii) From 2 nd February, the trade wind layer is largely modified with a sudden restoration of 39 strong easterlies at 700 hPa (Fig. 8e) associated with a deepening of the trade wind layer and an 40 increase of the inversion strength. However, surface easterlies remain weak for the season (Fig. 8d). 41 This 'intermediate' trade wind regime is so-called E3. 42 iv) From 5 th to 11 th February, the environment returns to its mean climatological features 43 with both an increase in the surface trade winds and stronger subsiding motion in mid-troposphere 44 coinciding with a westward extent of the subtropical North Atlantic high (not shown). This synoptic 45 regime results in both a moistening of the boundary layer (increase of PW) and weaker inversion 46 strength, leading to an increase in significant rainy events. This environment, more representative of 47 a climatological 'trade wind regime', is called E4. 48 We hereby investigate whether the four distinct large-scale environments (E1, E2, E3, E4) 4 identified in the previous section can be associated with different spatial mesoscale cloud 5 organisations observed in geostationary images (Fig. 9, first line). The model shows a very good 6 ability to reproduce the large variety of mesoscale pattern throughout the EUREC4A campaign 7 (https://observations.ipsl.fr/aeris/eurec4a/) but in this study we focus on particular days for which a 8 HALO flight has been conducted. The choice of the cases and their following description largely 9 rely on both the HALO flight reports and the C 3 ONTEXT (Common Consensus on Convective 10 OrgaNizaTion during the EUREC 4 A eXperimenT) classification based on satellite observations 11 [START_REF] Schulz | C 3 ONTEXT: A Common Consensus on Convective OrgaNizaTion during the 20 EUREC 4 A eXperimenT[END_REF]): 12

i) The 24 th January at 1200 UTC (Fig. 8 black dot) is characterized by an unusually deep (4 13 km) convection overshooting the trade wind inversion producing extensive stratiform clouds which 14 are the remnants of the apparent fishbone-like skeleton. The presence of evident cold-pool to the 15 north of the HALO circle is also mentioned in the flight report. According to the classification of 16 Stevens et al. (2020), the appearance of this mesoscale cloud organisation can be associated with a 17 Fish pattern (FI in the following). Following the C 3 ONTEXT classification of Schulz (2022), 22 nd 18 January would appear to be a better choice for the representation of the Fish environment (E1) 19 nevertheless when HALO flew, the FI cloud patterns were much more prevalent on 24 th February. 20

ii) To illustrate the environment E2, the 28 th January at 1800 UTC (Fig. 8 blue dot) is 21 selected. It is dominated by scattered shallow convection except in the very north with a remnant of 22 a northwest-southeast oriented line of flower-like elements. This case is close to a Sugar cloud 23 organisation (SU in the following). 24 iii) For the environment E3, the 2 nd February at 1800 UTC (Fig. 8 The ability of AROME-OM to represent these four aforementioned cases is now assessed, 38 both in its current configuration and in its future configuration at a 1.3 km resolution. This 39 evaluation is also carried out against the analyses of IFS at a 12.5 km horizontal grid spacing. [START_REF] Nuijens | The behavior of trade-wind 41 cloudiness in observations and models: The major cloud components and their 42 variability[END_REF] Figures 9 andS1 (first row) present the cloud organisation as observed by geostationary 41 satellite observations. Focusing on the scene of the HALO circle (Figure S1), the size of the cloud 42 pattern depends widely on the organisations: SU is mostly populated by unorganized and very small 43 cloud patterns, FI is dominated by large organisation of about 100 km wide, FL is more 44 characterized by circular clumped structures of about 50-100 km whilst GR shows more linear 45 patterns organized along lines of a few tens of km (yellow arrows). Both configurations of 46 AROME-OM (second and third rows) show a good ability to reproduce the main features of these 47 distinct organisations such as the mesoscale fish-bone like structure on the southern side of the 48 HALO circle, the apparent gust front along which FL and GR clouds pattern form (Fig. S1, dashed 49 yellow lines) as well as the large areas of clear air that surround FI and FL structures. Compared to the 50 satellite images, the loss of accuracy in the structure of the clouds in both runs of AROME-OM is 1 partly linked with their effective resolution which is about 22-25 km for the current configuration 2 and 12 km for the future configuration. This is particularly evident for the finer structures as SU and 3 GR (Fig. S1). The benefit from an increase of resolution of AROME-OM is substantial on the 4 eastern boundary of the domain, where finer structures are better caught at a 1.3 km resolution (Fig. 5 9). The evaluation of AROME-OM is carried out in the vicinity of the HALO circle which is quite 6 far from the eastern side of the domain which may be strongly influenced by its forcing model. IFS 7 partly fails to mimic the main cloud organisations (Fig. 9), except for the extensive stratiform 8 clouds or flower-like structure, highlighting the need of a kilometre-scale model to reproduce the 9 four main mesocale patterns described in [START_REF] Stevens | 33 Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds[END_REF]. [START_REF] Bock | Integrated water vapour observations in the Caribbean arc from a network of ground-12 based GNSS receivers during EUREC 4 A[END_REF] In both the models and the satellite observations, the patches of cloud fraction greater than 11 80% (Fig. 10, blue contour) roughly coincide with PW greater than 36 mm (Fig. 10, in orange), 12 especially for GR and FL cases. PW exhibits strong mesocale variability which suggests a major 13 role of the low-level convergence leading to a fast increase of PW, rather well captured by both 14 versions of AROME-OM. For instance, PW in the FI case ranges from 30 mm north of the HALO 15 circle to about 50 mm southeast. This mesocale pattern is dominated by the most extensive patches 16 of the highest values of PW (> 50 mm) that are the footprint of the fishbone-like skeleton. The GR 17 type is rather populated by isolated strong peaks of PW (~50 mm) that do not exceed a horizontal 18 extension of about 10 km. FI and GR types are probably the two cases with the most extensive 19 cloud depth. The AROME-OM vertical-longitudinal cross section of the relative humidity (Fig. S2, 20 2 nd line) indeed reveals that the cloud top height reaches 4 km (550/600 hPa) for FI and 5 km 21 (500/550 hPa) for GR. Although a little overestimated, this relative deep extension of the cumulus 22 was supported by the observations during the HALO flight. The joint examination of the spatial 23 distribution of the cloud fraction and the PW (Fig. S1 and Fig. 10) does not allow to determine a 24 PW threshold below which large-scale area of suppressed clouds occur. For instance, for the SU 25 case, the extensive area of clear sky in the region of the HALO exhibits PW ranging from 24 mm to 26 the north-east to 36 mm to the southwest. To better understand the relationship between PW and 27 cloud occurrence, further investigations must be undertaken. LES would be a suitable tool to 28 disentangle the mechanisms at play and their key spatio-temporal scales. In particular, the question 29 is open as to whether the positive anomalies in PW are due to enhanced boundary layer convective 30 activity, to shallow convection outflow (Narenpitak et The large-scale environment in which the four cases form is assessed against a unique 36 dataset of dropsondes launched along the HALO circle. The evaluation procedure is detailed in 37 section 2.3. 38 Figure 11 shows a comparison of the distribution of each parameter along the HALO circle 39 for the four cases. The distribution of both AROME-OM and IFS roughly agrees with that of 40 dropsondes, with a little benefit for AROME-OM, for the near surface temperature and relative 41 humidity, as well as PW in three out of four cases. The overestimation of the inversion strength in 42 AROME-OM (~+2K) appears to be partly controlled by its forcing model (~+1K). This may be 43 indicative of an overestimation of the large-scale-subsidence in free troposphere but the 44 examination of the vertical profile of the vertical velocities (Fig. 12 for AROME-OM, not shown for 45 IFS) does not allow the confirmation of this hypothesis. Similarly, the large PW overestimation in 46 AROME-OM for FI and GR cases (+4mm) is likely driven by that of IFS (+5 to + 6mm), even if 47 this bias is not observed for the two other cases. Both near surface wind speed and near surface 48 temperature exhibit weak biases (resp. < 1m/s and <0.5K) which are in good agreement with the 49 biases found at the BCO during the three winter seasons (Figure 7). 50

Figure 12 compares the thermodynamical profile of AROME-OM against the mean

F o r P e e r R e v i e w
1 profile of about twelve dropsondes. Overall, the biases of the four cases are consistent with those 2 previously identified in the radiosoundings at Grantley Adams Airport during the three winter 3 seasons (2018-2020), namely a cold bias in the cloud layer (~-1 K 930-800 hPa), a dry bias (~-1 4 g/kg) in the subcloud layer as well as an overestimation of the trade winds (~1m/s) below 850 hPa. 5 The AROME-OM vertical velocity profiles roughly coincide with those of the dropsondes, even if 6 the magnitude of the upward motion in the cloud and subcloud layers of the GR case is 7 underestimated. 8 To summarize, in spite of weak biases, AROME-OM is able to represent the variety of 9 large-scale environments in which the main mesocale organisations form. 10 The examination of the thermodynamical profiles (Fig. 12) allows us to discriminate the 19 four cases into two thermodynamically distinct environments : a first including the FI and SU cases 20 (so-called 'FISU') with a shallow trade wind layer (1 rst line) and a second one including FL and GR 21 cases (so-called 'FLGR') with a deep trade wind layer penetrating up to 600 hPa (2 nd line). This 22 difference of environment occurs throughout the Caribbean domain (Fig. S2, The FI and SU environments differ greatly in their estimated inversion strength. As 26 discussed in section 4.1, this FI case is driven by an extratropical wind shear line leading to an 27 increase of low-level convergence and moistening (PW~40 mm, Fig. 11) which in turn favours 28 instability (EIS~0 K, Fig. 11). The strong upward motion in the SCL (Fig. 12) supports the vertical 29 development of cumulus up to 600 hPa as exhibited on the vertical profile of specific humidity (Fig. 30 12). In contrast to the FI case, the SU case is characterized by a somewhat stronger inversion 31 strength (EIS~+2-3 K, Fig. 11). Disentangling mechanisms responsible for such differences between 32 FI and SU is rather complex, but a finer exploration of the causes of the sudden stilling in the trade 33 wind regime provides a hint. Here, the light winds are associated with a loose horizontal pressure 34 gradient in relation with a westward shift of the subtropical highs over Southern Europe (not 35 shown). In agreement with the findings of [START_REF] Nuijens | The influence of wind speed on shallow marine cumulus 37 convection[END_REF], a slackening in the trade wind 36 regime is correlated with low PW (Fig. 7, see between the blue vertical line and the two days 37 before). In turn, low PW favours both stability and subsiding motion in the SCL, as exhibited in the 38 vertical profile. This case is consistent with the works of [START_REF] Bony | Sugar, gravel, fish and flowers: Dependence of 19 mesoscale patterns of trade-wind clouds on environmental conditions[END_REF] where during the winter 39 2009-2010 (their Fig. 4e) SU pattern can occur in such a stable environment. [START_REF] Nuijens | The behavior of trade-wind 41 cloudiness in observations and models: The major cloud components and their 42 variability[END_REF] The FL and GR case environments also significantly differ by their inversion strength. The 41 GR case arises in an unstable environment with an estimated inversion strength close to 0 (Fig. 11). 42 Examination of the vertical profile of potential temperature (Fig. 12) confirms that this case is the 43 most unstable among the four cases even if the signature of the clouds is not visible on the vertical 44 profile of the specific humidity since no deep cumulus crosses the HALO circle but rather lies 45 inside. The vertical extension of cumulus up to 4 km (Fig. S2 ~600 hPa) is probably fuelled by the 46 occurrence of strong upward motions in the subcloud layer (Fig. 12). The ability of the strong 47 subsidence in mid-troposphere in patterning the mesoscale shallow convection need more in depth-48 studies. In contrast to the GR case, the FL case is associated with a strong inversion strength 49 (EIS~+4-5 K, Fig. 11). In agreement with [START_REF] Schulz | Characterization and Evolution of Organized Shallow 17 Convection in the Downstream North Atlantic Trades[END_REF], the cloud top is much lower than in 50 the GR case (Fig. S2, 2 nd line) and spread below a 700-750 hPa layer of strong stability (Fig. 12, see 1 potential temperature). This case exhibits significant subsiding motions in the SCL (Fig. 12, red 2 lines) which probably represent the footprint of a cold pool covering the major area of the HALO 3 circle. In agreement with [START_REF] Bock | Integrated water vapour observations in the Caribbean arc from a network of ground-12 based GNSS receivers during EUREC 4 A[END_REF], it could be hypothesized that in a windy environment 4 the difference of organisation between FL and GR is not driven by the PW since the same amounts 5 of PW are observed for both cases (Fig. 11), which differs greatly from the environment of the 6 FISU family where PW plays a discriminating role. 7 To conclude, it may be argued that AROME-OM captures the large variety of environment 8 in which the four main organisations identified by [START_REF] Stevens | 33 Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds[END_REF] form. The four illustrated 9 cases are in agreement with the past studies, except for the FI case which is characterized by a much 10 stronger inversion strength than usually observed [START_REF] Bony | Sugar, gravel, fish and flowers: Dependence of 19 mesoscale patterns of trade-wind clouds on environmental conditions[END_REF], their Fig. 4e). 11 12 6. Conclusions 13 14 The major aim of this study was to investigate the potential of the French operational 15 convection-permitting model AROME-OM in order to represent the shallow convection in the 16 downwind trade regime. The principal findings are based on a set of three wintertime (January-17 February 2018-2020) 2.5 km resolution simulations performed across a very large domain (2000 x 18 1200 km²) with lateral boundary conditions provided by IFS. A subdomain is defined (1000 x 500 19 km²) in order to take advantage of the international EUREC 4 A field campaign which took place to 20 the east of Barbados in January-February 2020. In terms of observations, this study also benefits 21 from in-situ measurements which have been collected at the Grantley Adams Airport and at the 22 BCO platform which are both representative of the environment in which trade wind cumuli 23 develop. 24 The evaluation of the model against the soundings of Grantley Adams Airport revealed an 25 excessive deepening of the boundary layer leading to a cold and dry bias, along with trade winds 26 which were too strong. One objective of this study was to investigate the ability of AROME-OM to 27 capture the double peak of cloud fraction [START_REF] Nuijens | The distribution and variability 39 of low-level cloud in the North Atlantic trades[END_REF], with a first peak near the cloud base 28 (~920 hPa) and a second peak near the top (~820 hPa) of the deepest cumuli. Even though the 29 shallow convection is parametrized as in most kilometric-scale horizontal resolution models, the 30 model shows a good ability to simulate a larger variance in cloudiness near cloud top rather than 31 near cloud base, in agreement with observations [START_REF] Schulz | Characterization and Evolution of Organized Shallow 17 Convection in the Downstream North Atlantic Trades[END_REF]. Cloud depth is overall a little 32 underestimated in AROME-OM and the cloud fraction near the top of the deepest cumuli is too 33 strong. This suggests that additional calibration of the vertical mixing of the shallow convection 34 scheme which strongly affects the depth of the boundary layer [START_REF] Heim | Inter-model Variability in Convection-Resolving 46 Simulations of Subtropical Marine Low Clouds[END_REF] is required. The 35 analysis of the cloudiness sensitivity to the large-scale environment indicates a significant increase 36 at the cloud top for colder surface temperature and low PW, which probably corresponds to FL or 37 FI cloud organisations. In agreement with [START_REF] Nuijens | The influence of wind speed on shallow marine cumulus 37 convection[END_REF], it has also been found that 38 the cloud deepens in conditions of stronger winds, with the caveat that the increase of cloudiness 39 near the cloud base is poorly represented in AROME-OM. ) suggested that organisation is the result of combined effect of various 43 parameters such as surface wind speed, lower-tropospheric stability and vertical velocity in mid 44 and/or low troposphere. The EUREC 4 A field campaign allowed us to show the good ability of 45 AROME-OM at a 2.5 km resolution to represent the large variety of organisations that occur in 46 boreal winter conditions in the North Atlantic trades [START_REF] Stevens | 33 Sugar, gravel, fish and flowers: Mesoscale cloud patterns in the trade winds[END_REF]). This study focuses on 47 four case studies that were selected during this campaign on the basis of the HALO flight reports 48 and of the C 3 ONTEXT classification (Schulz 2022). Our analysis reveals that AROME-OM gives a 49 much better description of the cloud organisation than a model at lower resolution such as IFS for 50 those cases. The model shows a good ability to describe the different environments in which the In this study, we have focused on the sensitivity of the organisation to the environment but 14 further work is necessary in order to explore which processes are involved in the aggregation of the 15 shallow convection into mesoscale organisations. In particular the role of cold pools (Touzé-Peiffer 16 et al., 2021) or diabatic heating [START_REF] Bretherton | Understanding mesoscale aggregation of shallow cumulus 27 convection using large-eddy simulation[END_REF]

  [START_REF] Nuijens | The distribution and variability 39 of low-level cloud in the North Atlantic trades[END_REF]). The occurrence of cloud is only considered when cloud fraction is 2 above 0.01. 3

4 5 3 .

 43 statistical algorithm. Errors on the PW retrieved from the AMSR2 family are expected to be 2 lower than 1 mm (Wentz and Meissner 2000).3 Ability of AROME-OM to represent the wintertime shallow convection 6 7 3.1 Evaluation of the thermodynamical profiles 8 9

  et al. (2021) highlighted the same shortcoming in the ERA5 23 reanalysis. The underestimation of precipitation in AROME-OM in intermediate range of intensity 24 (from 1 to 10 mm/day) found by Faure et al. (

3 4 4 . 5 mesoscale

 345 to support the results of Fig.6concerning the sensitivity of the cloud fraction to 2 the large-scale environment. Ability of AROME-OM to represent the different shallow convection

  Selection of four cases representative of the distinct large-scale environments 2 3

  red dot) is chosen. It is 25 characterized in the vicinity of the HALO circle by the occurrence of flowers-structure (FL in the 26 following) associated with marked cold pools. The physical mechanisms implied in such a cloud 27 pattern are fully documented by recent studies based on LES (Narenpitak et al. 2021, Dauhut et al., 28 2022). 29 iv) For the environment E4, the 9 th February at 1200 UTC (Fig.8 yellow dot) is selected. 30 The scene of the HALO circle was dominated by streets of cloud cumulus in development with 31 occasionally larger stratiform cloud aggregation associated with precipitation. This variety of 32 vertical extension (up to 3-4 km) with clouds organized along lines, sometimes resembling cold 33 pools, may correspond to a Gravel cloud organisation (GR in the following). 34 35 4.3 Ability of AROME-OM to reproduce the four cases 36 37

11 4 . 5 .

 45 Documentation of the air masses of the four case studies 12 13 Recent investigations (Bony et al. 2020, Schulz et al. 2021) have shown that the near-14 surface wind speed and the strength of the lower-tropospheric stability play a discriminating role on 15 the occurrence of the mesocale patterns in the western North Atlantic trade wind region. This 16 following section hereby investigates to what extent the variety of large-scale environment 17 identified among the 4 case studies adheres these findings. 18

  1 st line) and is rather 23 consistent with the findings of Bony et al. (2020) who have shown that FL and GR tend to form 24 when the trade wind are strong. 25

  40 One may also wonder if air mass characteristics are indicative of the different mesoscale 41 cloud organisations. Earlier studies (Schulz et al. 2021, Bony et al. 2020, Aemisegger et al. 2021, 42 Medeiros and Stevens 2011

  form, in line with the EUREC4 A observations, with weak biases consistent with the 2 biases found when comparing to the long-term observations at the BCO. In agreement with past 3 studies[START_REF] Bony | Sugar, gravel, fish and flowers: Dependence of 19 mesoscale patterns of trade-wind clouds on environmental conditions[END_REF], Schulz et al. 2021), the FI and SU cases occur in a calm environment 4 associated with a shallow trade wind layer whilst the GR and FL cases arise under the influence of a 5 windy environment characterized by a deep trade wind layer. This study is also in phase with the 6 work of[START_REF] Bock | Integrated water vapour observations in the Caribbean arc from a network of ground-12 based GNSS receivers during EUREC 4 A[END_REF] which revealed that the amount of PW does not allow to distinguish the 7 patterns, except for FI. The main point of divergence with earlier studies[START_REF] Bony | Sugar, gravel, fish and flowers: Dependence of 19 mesoscale patterns of trade-wind clouds on environmental conditions[END_REF]) is 8 related to the FI case since it is characterised by stability much lower than usually observed. A 9 closely related question is how vertical velocities matters for the organisation of mesocale shallow 10 convection. In agreement with[START_REF] Dauhut | The Flower trade-wind clouds are shallow mesoscale 31 convective systems[END_REF], the four case studies highlight that mesocale 11 vertical motions represent the imprint of the cloud systems, but the role of vertical velocities at 12 larger scale (hundreds of km) on the cloud organisation remains an open question.13

F

  Figure Number (* with lowpass filter)

  al. 2021, Bretherton and Blossey 2017, 31 Dauhut et al. 2022), or former cloud clusters which dissipate. 32 33 4.4 Assessment of the large-scale environment during the four case studies against the dropsondes 34 35

  merit further investigation. The changes in 17 organisation that occur across the subtropical Atlantic Ocean could also be explored in virtue of the 18 use of such kilometric convection-permitting model in order to conduct simulations over vast 19 domains for several weeks. Vogel, R, Nuijens, L, Stevens, B. 2020. Influence of deepening and mesoscale organization of 2 shallow convection on stratiform cloudiness in the downstream trades. Q J R Meteorol Soc.; 3 146: 174-185. 4 Wentz, F. J., and T. Meissner. 2000: AMSR ocean algorithm, version 2. Remote Sensing Systems 5 Tech. Rep. 121599A-1, 66 pp. [Available online at http://eospso.gsfc.nasa.gov/sites/default/ 6 Wood, R., & Bretherton, C. S. 2006. On the Relationship between Stratiform Low Cloud Cover and Lower-Tropospheric Stability, Journal of Climate, 19(24), 6425-6432 Page 22 of 37 Quarterly Journal of the Royal Meteorological Society

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Acknowledgements 22

The authors are grateful to the reviewers and Marie Lothon for their thoughtful and useful 23 comments that have greatly improved the original manuscript and Yohan Brunet for his