Modeling Cloud Dynamics for Accurate Climate Prediction

Research Overview

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Resnick Fellow, Ignacio Lopez Gomez, is a member of a research team developing a mathematical model that represents clouds in climate models. The team's two recent publications, "A Generalized Mixing Length Closure for Eddy‐Diffusivity Mass‐Flux Schemes of Turbulence and Convection" and "Unified Entrainment and Detrainment Closures for Extended Eddy‐diffusivity Mass‐Flux Schemes", describe the mathematical model of the processes most strongly affecting cloud formation in the atmosphere: turbulence and convection. The papers show that the model will lead to more accurate simulations of clouds in climate models, which addresses one of the biggest sources of uncertainty in them.

Scientific Achievement

We developed a mathematical model that represents clouds in climate models faithfully.

A sketch of some of the processes captured by the proposed cloud model, including turbulence and convection. The box represents a single column within a climate model. Climate models rely on simplified mathematical models to represent all processes that have a scale smaller than the column width. Image courtesy the author (Schneider Research Group).

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A sketch of some of the processes captured by the proposed cloud model, including turbulence and convection. The box represents a single column within a climate model. Climate models rely on simplified mathematical models to represent all processes that have a scale smaller than the column width. Image courtesy the author (Schneider Research Group).

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Significance and Impact

The leading source of uncertainty in climate projections can be traced back to the inability of climate models to represent clouds. Our model provides a way forward.

Vertical profiles of (a) potential temperature, (b) specific humidity and (c) liquid water content predicted by the proposed cloud model (SCM) at different resolutions for a stratocumulus cloud deck off the coast of San Diego, CA. Results compare well with high-fidelity numerical simulations (LES) and observations. The shaded region is a measure of uncertainty from numerical simulations. The uncertainty in liquid water content, or occurrence of cloud, is very high in current models. Our model compares well with observations. Image Credit: Lopez‐Gomez et al, 2020. https://doi.org/10.1029/2020MS002161

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Vertical profiles of (a) potential temperature, (b) specific humidity and (c) liquid water content predicted by the proposed cloud model (SCM) at different resolutions for a stratocumulus cloud deck off the coast of San Diego, CA. Results compare well with high-fidelity numerical simulations (LES) and observations. The shaded region is a measure of uncertainty from numerical simulations. The uncertainty in liquid water content, or occurrence of cloud, is very high in current models. Our model compares well with observations. Image Credit: Lopez‐Gomez et al, 2020. https://doi.org/10.1029/2020MS002161

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Technical Details

• The model is tested for stratocumulus, cumulus and cumulonimbus clouds. Weather and climate models struggle to correctly represent stratocumulus clouds, which are common off the coast of California, Peru and Namibia. In our study, we demonstrate the prediction accuracy of our proposed cloud model for this type of cloud.
• The model is time-dependent and captures the diurnal cycle of convection well. Climate models have important biases regarding the onset time of precipitation, which is related to the diurnal cycle of convection. Our model predictions show good agreement with high-fidelity numerical simulations.

Diurnal cycle of convection in the Amazon rainforest (left column) and in the Southern Great Plains, Oklahoma (right column). Contours show updraft vertical velocity from high-fidelity numerical simulations (first row) and the proposed cloud model (second row). The third row shows the liquid water path (LWP), a measure of cloudiness, predicted by the cloud model (green) and high-fidelity numerical simulations (gray). The bottom row shows the surface latent heat flux (blue) and sensible heat flux (red). Image credit: Cohen et al, 2020. https://doi.org/10.1029/2020MS002162

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Diurnal cycle of convection in the Amazon rainforest (left column) and in the Southern Great Plains, Oklahoma (right column). Contours show updraft vertical velocity from high-fidelity numerical simulations (first row) and the proposed cloud model (second row). The third row shows the liquid water path (LWP), a measure of cloudiness, predicted by the cloud model (green) and high-fidelity numerical simulations (gray). The bottom row shows the surface latent heat flux (blue) and sensible heat flux (red). Image credit: Cohen et al, 2020. https://doi.org/10.1029/2020MS002162

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Lopez‐Gomez, I., Cohen, Y., He, J., Jaruga, A., & Schneider, T. A generalized mixing length closure for eddy‐diffusivity mass‐flux schemes of turbulence and convection. Journal of Advances in Modeling Earth Systems (2000) 12, e2020MS002161. https://doi.org/10.1029/2020MS002161

Cohen, Y., Lopez‐Gomez, I., Jaruga, A., He, J., Kaul, C. M., & Schneider, T. Unified entrainment and detrainment closures for extended eddy‐diffusivity mass‐flux schemes. Journal of Advances in Modeling Earth Systems (2020) 12, e2020MS002162. https://doi.org/10.1029/2020MS002162

Contact: Tapio Schneider