Process‐oriented evaluation of the oversea <scp>AROME</scp> configuration: Focus on the representation of cloud organisation

Beucher, Florent; Couvreux, Fleur; Bouniol, Dominique; Faure, Ghislain; Favot, Florence; Dauhut, Thibaut; Ayet, Alex

doi:10.1002/qj.4354

Cited by 8 publications

(19 citation statements)

References 61 publications

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“…February 2 was selected as a convenient date, with a cloud residence time of about 7 hr in the 200‐km large domain of the LES. Even if a mild zonal wind at 10 m altitude is unusual for flower clouds (Bony et al ., 2020), the February 2 zonal wind was much stronger at higher altitude (9–10 m

\cdotp

{}^{-1}

at 3 km: Beucher et al ., 2022).…”

Section: Case Study High‐resolution Observations and Modelmentioning

confidence: 99%

“…The model was initialised at 1200 UTC on February 2, 2020 and ran for 7 hr. The time step is 1 s. The initial and lateral boundary conditions come from the AROME‐OM forecast on February 2, 2020 that ran with a horizontal grid spacing of 1.3 km (Beucher et al ., 2022). The Meso‐NH atmosphere is initialised with the AROME‐OM fields after 12 hr of integration of AROME‐OM, interpolated on the LES grid.…”

Section: Case Study High‐resolution Observations and Modelmentioning

confidence: 99%

“…The LES tends to overestimate the frequencies of the lowest and largest values compared with the HAMP radiometer. In line with this, the LES presents lower average PW but larger standard deviation than the observations (30.8

\pm

4.4 kg

\cdotp

{}^{-2}

versus 31.0

\pm

3.6 kg

\cdotp

{}^{-2}

), consistent with the forcing model AROME‐OM (figure 11a in Beucher et al ., 2022). The distribution of LWP shows a clear maximum at low values and a steady decrease towards larger values.…”

Section: Characterisation Of Flower Clouds and Their Environmentmentioning

confidence: 99%

“…This is consistent with the clear‐sky potential temperature profile (Figure 6e). This bias is probably inherited from the forcing model (Beucher et al ., 2022). For the highest clouds, the LES tends to be too moist throughout the average profile, up to 2,300 m. This might be due to the fact that comparison of dropsonde profiles with LES column profiles assumes implicitly that the dropsondes fall vertically.…”

Section: Characterisation Of Flower Clouds and Their Environmentmentioning

confidence: 99%

“…To investigate the convective processes at play, the numerical modelling approach is convenient, as it provides comprehensive and consistent 4D fields of dynamical and thermodynamical variables. Cloud‐resolving simulations using kilometric horizontal grid spacing can represent the mesoscale organisation and its day‐to‐day variability successfully (Beucher et al ., 2022). However, they employ a parametrisation of shallow convection and therefore do not resolve the cumulus explicitly, impeding investigation of the fine‐scale circulations at play.…”

Section: Introductionmentioning

confidence: 99%

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Flower trade‐wind clouds are shallow mesoscale convective systems

Dauhut

Couvreux

Bouniol

et al. 2022

Quart J Royal Meteoro Soc

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Flower clouds are trade‐wind shallow cumuli, with tops reaching 3 km altitude, organised into 100‐km wide clusters. They are widespread over the subtropics and associated with the strongest cloud radiative effect among trade‐wind cumuli mesoscale organisations. In the context of large uncertainty in climate projections due to the representation of shallow clouds, major knowledge gaps remain about the global impact of mesoscale organisations and the local processes driving them. Here, the processes governing the flower organisation are investigated based on the case study of February 2, 2020 from the Elucidate the Couplings Between Clouds, Convection, and Circulation (EUREC4$$ {}^4 $$A) campaign, east of Barbados. One flower cloud is simulated with a large‐eddy simulation (LES), using the Meso‐NH model at 100‐m horizontal grid spacing, and validated extensively with high‐resolution observations from the High Altitude and Long‐range Research Aircraft (HALO), dropsondes, and satellite measurements. The cloud‐top altitudes exhibit a trimodal distribution. The processes shaping flower clouds are wide cold pools and cloudy updrafts organised in one large arc at the western edge. These updrafts are responsible for the highest cloud tops and drive most of the vertical turbulent fluxes of sensible heat, humidity, and momentum. A mesoscale circulation takes place at the scale of the flower clouds and makes them very similar to deep mesoscale convective systems.

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\cdotp

{}^{-1}

at 3 km: Beucher et al ., 2022).…”

Section: Case Study High‐resolution Observations and Modelmentioning

confidence: 99%

Section: Case Study High‐resolution Observations and Modelmentioning

confidence: 99%

\pm

4.4 kg

\cdotp

{}^{-2}

versus 31.0

\pm

3.6 kg

\cdotp

{}^{-2}

), consistent with the forcing model AROME‐OM (figure 11a in Beucher et al ., 2022). The distribution of LWP shows a clear maximum at low values and a steady decrease towards larger values.…”

Section: Characterisation Of Flower Clouds and Their Environmentmentioning

confidence: 99%

Section: Characterisation Of Flower Clouds and Their Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Flower trade‐wind clouds are shallow mesoscale convective systems

Dauhut

Couvreux

Bouniol

et al. 2022

Quart J Royal Meteoro Soc

Self Cite

View full text Add to dashboard Cite

show abstract

Kilometer‐Scale Simulations of Trade‐Wind Cumulus Capture Processes of Mesoscale Organization

Saffin

Lock

Tomassini

et al. 2023

J Adv Model Earth Syst

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The EUREC4A (Elucidating the role of clouds–circulation coupling in climate) and ATOMIC (Atlantic Tradewind Ocean‐Atmosphere Mesoscale Interaction Campaign) joint field campaign gathered observations to better understand the links between trade‐wind cumulus clouds, their organization, and larger scales, a large source of uncertainty in climate projections. A recent large‐eddy simulation study showed a cloud transition that occurred during this field campaign, where small shallow clouds developed into larger clouds with detrainment layers, was caused by an increase in mesoscale organization generated by a dynamical feedback in mesoscale vertical velocities. We show that kilometer‐scale simulations with the Met Office's Unified Model reproduce this increase in mesoscale organization and the process generating it, despite being much lower resolution. The timing of development is associated with large‐scale convergence. Sensitivity tests with a shorter spin‐up time, to reduce initial organization, still have the same timing of development and sensitivity tests with cold pools suppressed show only a small effect on mesoscale organization. Mesoscale organization and clouds are sensitive to resolution, with higher resolution simulations producing weaker organization and less cloud, which affects changes in net radiation. The clouds also have substantial differences to observations. Therefore, while kilometer‐scale simulations can be useful for understanding processes of mesoscale organization and links with large scales, including responses to climate change, simulations will still suffer from significant errors and uncertainties in radiative budgets.

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Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades

Jansson,

Janssens,

Grönqvist

et al. 2023

J Adv Model Earth Syst

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Small shallow cumulus clouds (<1 km) over the tropical oceans appear to possess the ability to self‐organize into mesoscale (10–100 km) patterns. To better understand the processes leading to such self‐organized convection, we present Cloud Botany, an ensemble of 103 large‐eddy simulations on domains of 150 km, produced by the Dutch Atmospheric Large Eddy Simulation model on supercomputer Fugaku. Each simulation is run in an idealized, fixed, larger‐scale environment, controlled by six free parameters. We vary these over characteristic ranges for the winter trades, including parameter combinations observed during the EUREC4A (Elucidating the role of clouds–circulation coupling in climate) field campaign. In contrast to simulation setups striving for maximum realism, Cloud Botany provides a platform for studying idealized, and therefore more clearly interpretable causal relationships between conditions in the larger‐scale environment and patterns in mesoscale, self‐organized shallow convection. We find that any simulation that supports cumulus clouds eventually develops mesoscale patterns in their cloud fields. We also find a rich variety in these patterns as our control parameters change, including cold pools lined by cloudy arcs, bands of cross‐wind clouds and aggregated patches, sometimes topped by thin anvils. Many of these features are similar to cloud patterns found in nature. The published data set consists of raw simulation output on full 3D grids and 2D cross‐sections, as well as post‐processed quantities aggregated over the vertical (2D), horizontal (1D) and all spatial dimensions (time‐series). The data set is directly accessible from Python through the use of the EUREC4A intake catalog.

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

Cited by 8 publications

References 61 publications

Flower trade‐wind clouds are shallow mesoscale convective systems

Flower trade‐wind clouds are shallow mesoscale convective systems

Kilometer‐Scale Simulations of Trade‐Wind Cumulus Capture Processes of Mesoscale Organization

Cloud Botany: Shallow Cumulus Clouds in an Ensemble of Idealized Large‐Domain Large‐Eddy Simulations of the Trades

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