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Atmospheric Convection as an Unstable Predator–Prey Process with Memory

Maxime ColinaClimate Change Research Centre, University of New South Wales Sydney, Kensington, New South Wales, Australia
bARC Centre of Excellence for Climate System Science, University of New South Wales Sydney, Kensington, New South Wales, Australia
cLaboratoire de Météorologie Dynamique/IPSL, Sorbonne Université, Paris, France
dLaboratoire GEPASUD, University of French Polynesia, Punaauia, Tahiti, French Polynesia

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Steven C. SherwoodaClimate Change Research Centre, University of New South Wales Sydney, Kensington, New South Wales, Australia

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Abstract

Heuristic models and observational analyses of atmospheric convection often assume that convective activity, for example, rain rate, approaches some given value for any given large-scale (“macrostate”) environmental conditions, such as static stability and humidity. We present novel convection-resolving simulations in which the convective activity evolves in a fixed equilibrium mean state (“macrostate”). In this case, convective activity is unstable, diverging quasi exponentially away from equilibrium either to extreme or zero rain rate. Thus, almost any rain rate can coexist with an equilibrium profile: the model rain rate also depends on convective history. We then present a two-variable, predator–prey model motivated by this behavior, wherein small-scale (“microstate”) variability is produced by but also promotes convective precipitation, while macrostate properties such as CAPE promote but are consumed by convective precipitation. In this model, convection is influenced as much by its own history (via persistent microstate variability) as by its current environment. This model reproduces the simulated instability found above and could account for several lag relationships in simulated and observed convection, including its afternoon maximum over land and the well-known “quasi-equilibrium” balance at synoptic time scales between the forcing and response of key variables. These results point to a strong role for convective memory and suggest that basic strategies for observing, modeling, and parameterizing convective processes should pay closer attention to persistent variability on scales smaller than that of the grid box.

Significance Statement

To better predict weather and climate, it is necessary to better understand the process that creates cumulonimbus clouds and associated rainfall: atmospheric convection. Here, we suggest that convective inertia (so-called convective memory) is a crucial aspect of convection. We run a new type of cloud simulation and detect an unsuspected kind of instability. To explain this, we introduce a predator–prey model of convection, in which the small-scale atmospheric structures are “hungry” for large-scale convective potential. The predator–prey model with memory recreates some typical convective phenomena. It is thus promising and calls for more attention to small-scale processes. The next question could be: How do we put the right amount of memory, the right prey, and the right predator into climate models?

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Both authors contributed equally to this manuscript.

Corresponding author: Maxime Colin, m.colin@unsw.edu.au, m.colin@unswalumni.com

Abstract

Heuristic models and observational analyses of atmospheric convection often assume that convective activity, for example, rain rate, approaches some given value for any given large-scale (“macrostate”) environmental conditions, such as static stability and humidity. We present novel convection-resolving simulations in which the convective activity evolves in a fixed equilibrium mean state (“macrostate”). In this case, convective activity is unstable, diverging quasi exponentially away from equilibrium either to extreme or zero rain rate. Thus, almost any rain rate can coexist with an equilibrium profile: the model rain rate also depends on convective history. We then present a two-variable, predator–prey model motivated by this behavior, wherein small-scale (“microstate”) variability is produced by but also promotes convective precipitation, while macrostate properties such as CAPE promote but are consumed by convective precipitation. In this model, convection is influenced as much by its own history (via persistent microstate variability) as by its current environment. This model reproduces the simulated instability found above and could account for several lag relationships in simulated and observed convection, including its afternoon maximum over land and the well-known “quasi-equilibrium” balance at synoptic time scales between the forcing and response of key variables. These results point to a strong role for convective memory and suggest that basic strategies for observing, modeling, and parameterizing convective processes should pay closer attention to persistent variability on scales smaller than that of the grid box.

Significance Statement

To better predict weather and climate, it is necessary to better understand the process that creates cumulonimbus clouds and associated rainfall: atmospheric convection. Here, we suggest that convective inertia (so-called convective memory) is a crucial aspect of convection. We run a new type of cloud simulation and detect an unsuspected kind of instability. To explain this, we introduce a predator–prey model of convection, in which the small-scale atmospheric structures are “hungry” for large-scale convective potential. The predator–prey model with memory recreates some typical convective phenomena. It is thus promising and calls for more attention to small-scale processes. The next question could be: How do we put the right amount of memory, the right prey, and the right predator into climate models?

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Both authors contributed equally to this manuscript.

Corresponding author: Maxime Colin, m.colin@unsw.edu.au, m.colin@unswalumni.com

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