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Oh, Gun Ho (Loren)

University of British Columbia

Increases in greenhouse gas concentrations lead to the warming of the atmosphere, which is further amplified by climate feedback processes. An accurate prediction of global temperature increase, however, is yet to be established; inter-model variability in climate sensitivity among global climate models remains large, and the largest source of uncertainty comes from the representation of clouds. The goal of this dissertation is to explore the detailed dynamics and thermodynamics of moist convection based on high-resolution simulations of the atmosphere. Of particular interest is the mass exchange process between individual clouds and the surrounding environment. Based on numerical simulations of both shallow and deep convection, the properties of individual clouds were expressed as statistical distributions using a power-law fit. This revealed that, despite conventional assumptions, bulk-plume estimates of turbulent mass exchange rates based on cloud size cannot be a reliable measure of entrainment. Numerical techniques based on the Gaussian Process (GP) regression and Bayesian inference were used to quantify the observed oscillatory behaviour in the statistical distribution of simulated clouds. Statistical distributions of individual cloud properties were highly variable, which made it difficult for traditional methods to identify the oscillatory behaviour. The GP regression method was used to evaluate the hypothesis about the underlying oscillatory evolution of the cloud field, and it successfully identified a 95-minute oscillation from the time-series. Lastly, the traditional bulk-plume approximation, used in the conventional representation of clouds in large-scale models, was evaluated. Equations describing the tendencies of individual cloud mass and tracer concentrations were introduced, and it was found that the bulk-plume assumptions overestimated the dilutive effect of entrainment and detrainment, whereas the newly introduced equations accurately diagnosed the changes in individual cloud properties. Directly calculating the fluxes that determine the changes in individual cloud properties revealed that local variations within individual clouds influenced the rate at which the clouds dilute, and the distribution of individual cloud dilution rates exhibited a large degree of variability. Improving our understanding of how individual clouds behave is essential in developing a cloud model that better represents the effect of moist convection in large-scale models of the atmosphere.

Text

2024-05-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/88337

Atmospheric Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/