Kilometer-scale simulations of trade-wind cumulus capture processes of mesoscale organization

Saffin, Leo; Lock, A. P.; Tomassini, Lorenzo; Blyth, Alan; Böing, Steven J.; Denby, Leif; Marsham, John H.

doi:10.1002/essoar.10511907.1

Cited by 3 publications

(6 citation statements)

References 22 publications

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“…That is, mesoscale circulations aggregate q tm and moist-static energy into more strongly convecting regions; they have a negative gross moist stability (Raymond et al, 2009). These findings are in line with the evolution predicted in case studies with idealised LESs (Bretherton & Blossey, 2017;Narenpitak et al, 2021;Janssens et al, 2023) and a numerical weather prediction model (Saffin et al, 2023). In fact, all terms in fig.…”

Section: Lacking Mesoscale Radiative Cooling Anomaliessupporting

confidence: 86%

“…In contrast to LESs departing from spatially homogeneous conditions or kilometrescale resolution mesoscale or global models, ICON-312 simultaneously represents synoptic variability, mesoscale processes and the large eddies of shallow convection. It also simulates longer time periods than other recent simulations of individual mesoscale weather events (Narenpitak et al, 2021;Dauhut et al, 2023;Saffin et al, 2023). Hence, the simulation allows both i) comparisons against the observed statistics of mesoscale vertical motion during EUREC 4 A (Bony et al, 2017), and ii) expansions of our view on the dominant mesoscale balances of shallow convection to the monthly time scale.…”

Section: S1-s2mentioning

confidence: 94%

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Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades

Janssens,

George,

Schulz

et al. 2024

Preprint

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Earth’s climate sensitivity depends on how shallow clouds in the trades respond to changes in the large-scale tropical circulation with warming. In all theory for this cloud-circulation coupling, it is assumed that the clouds are controlled by the field of vertical motion on horizontal scales larger than the convection’s depth (~1 km). Yet this assumption has been challenged both by recent in-situ observations, and idealised large-eddy simulations (LESs). Here, we therefore bring together the recent observations, new analysis from satellite data, and a forty-day, large-domain (1600 x 900 km2) LES of the North Atlantic from the 2020 EUREC4A field campaign, in search of new explanations for the interaction between shallow convection and vertical motions, on scales between 10-1000 km (mesoscales). Across all datasets, the shallow mesoscale vertical motions are consistently represented, ubiquitous, frequently organised into circulations, and formed without imprinting themselves on the mesoscale buoyancy field. This allows us to employ the weak-temperature gradient approximation, which shows that between at least 12.5-400 km scales, the vertical motion balances heating fluctuations in groups of precipitating shallow cumuli. That is, across the mesoscales, shallow convection controls the vertical motion in the trades, and does not simply adjust to it. In turn, the mesoscale convective heating patterns appear to consistently grow through moisture-convection feedback. Therefore, to represent and understand the cloud-circulation coupling of trade cumuli, the full range of scales between the synoptics and the hectometre must be included in our conceptual and numerical models.

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Section: Lacking Mesoscale Radiative Cooling Anomaliessupporting

confidence: 86%

Section: S1-s2mentioning

confidence: 94%

Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades

Janssens,

George,

Schulz

et al. 2024

Preprint

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“…By applying the trained neural network on tiles sampled along a trajectory that follow clouds as they evolve, we can map out transitions between organisational states and thereby investigate the drivers behind these transitions. We use a 4-day trajectory (created with lagtraj, Denby & Boeing, 2022), which follows a cloud-layer airmass across the Atlantic as organisation develops from ini--14- tial isolated scattered cumuli (Sugar ) into larger isolated cumuli (Flowers) (August 2nd 2020, studied by Saffin et al, 2023b;Narenpitak et al, 2021b). Tiles along the trajectory (examples in Figure 4b) are then mapped onto the embedding manifold (Figure 4a).…”

Section: Mapping the Temporal Evolution Of Organisationmentioning

confidence: 99%

“…In the context of the EUREC 4 A field campaign (Stevens et al, 2021) work by Stevens et al (2019) [S19] developed a set of four classifications for shallow cloud organisation by manual examination of visual satellite imagery. These classes were motivated by the physical processes expected important in different regimes, and have since formed the framing for many studies investigating the conditions under which different forms of organisation occur, both in observations (Bony et al, 2020;Schulz et al, 2021) [B21, S21] and in simulations (Dauhut et al, 2023), with particular focus on the transition from small to larger isolated detraining shallow clouds (Narenpitak et al, 2021a;Saffin et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

Charting the realms of Convective Cloud Organisation using Unsupervised Learning

Denby

2023

Preprint

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The representation of shallow tradewind cumulus clouds in climate models accounts for the majority of inter-model spread in climate projections, highlighting an urgent need to understand these clouds better. In particular, their spatial organisation appears to cause a strong impact of their radiative properties and dynamical evolution. The precise mechanisms driving different forms of convective organisation which arise both in nature and in simulations are, however, currently unknown. We show how the continuum of convective organisation states can be analysed as an emergent &#160;property of the embedding space representation learnt by a neural network through unsupervised learning. &#160;Specifically we will use a technique to extract an estimate of the manifold in a high-dimensional space on which possible states of convective organisation lie. &#160;Through composition of reanalysis and observations onto this manifold we are able to extract the characteristics of the atmosphere which coincide with different forms of convective organisation, and further we are able to map transitions between different states of organisation and study how these develop. We will show results from analysing: a) what the radiative properties of different forms of organisation are, b) what atmospheric characteristics coincide with different forms of organisation and c) what transitions occur when following air-masses along Lagrangian trajectories. &#160;Specifically, we find: a) net radiation changes significantly between different forms of organisation, b) agreement with previous studies on the importance of boundary layer wind-speed and to some degree atmospheric stability, and c) we are able to succinctly capture what transitions occur between regimes.

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“…The GOES satellite data was downloaded from French national center for Atmospheric data and services, AERIS, at https://observations.ipsl.fr/aeris/eurec4a-data/SATELLITES/GOES-E/2km_10min/ and ERA5 data was downloaded from the climate data store (Hersbach et al (2018b) and Hersbach et al (2018a)). The regridded data, trajectories, simulated satellite data, and remaining files required to reproduce the results in this study are available in a Zenodo repository (Saffin and Lock (2023), https://doi.org/10.5281/zenodo.7568386). The code used for data analysis is available at https://github.com/leosaffin/moisture_tracers.…”

Section: Appendix A: Simulated Satellite Observationsmentioning

confidence: 99%

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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Kilometer-scale simulations of trade-wind cumulus capture processes of mesoscale organization

Cited by 3 publications

References 22 publications

Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades

Shallow convective heating in weak temperature gradient balance explains mesoscale vertical motions in the trades

Charting the realms of Convective Cloud Organisation using Unsupervised Learning

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

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