Turbulence, clouds and climate models

Viernes 26 de enero de 2024 – 12:00 PM

IMPARTE: Dr. João Teixeira ┃ Invitado del grupo Interacción Micro y Mesoescala, ICAyCC

RESUMEN:

Clouds are known to have a remarkable and unique impact on climate, but unfortunately clouds are notoriously difficult to represent in climate models. The Intergovernmental Panel on Climate Change (IPCC) has reiterated that clouds remain the largest source of uncertainty in climate projections and currently it is not clear if clouds will amplify or reduce global warming. This is often referred to as the cloud-climate feedback problem. To first order, the cloud problem in climate models is essentially a turbulence and convection problem. Because the critical scales at which turbulent and convective mixing takes place are smaller than the typical climate (and global weather) prediction model’s horizontal grid size, equations for the dynamics of the statistics of turbulence and convection need to be developed: the turbulence and convection parameterization problem.

The representation (parameterization) of turbulent, cloud and convective mixing in global atmospheric models has been a major challenge in weather and climate research for several decades. In particular, different parameterizations are used, and often patched together artificially, for different types of convection: dry or moist, within the boundary layer or in the full troposphere. Traditionally, the eddy-diffusivity (ED) approach has been relatively successful in representing the properties of neutral and stable boundary layers, and surface layers in general.

The Mass-Flux (MF) approach, on the other hand, has been often used for the parameterization of shallow and deep moist convection. Recently, higher-order turbulence closures have been implemented in global models to represent boundary layer and shallow convection mixing. However, a fully unified parameterization of turbulence and convection (including deep convection) has never been successfully developed and implemented in atmospheric models. In this presentation, we review the cloud-climate feedback problem and the history of turbulence and convection in climate models, and discuss recent approaches based on optimal combinations of the ED and MF parameterizations (EDMF), and of higher-order closures with the multi-plume MF approach, as potential solutions for the full unification of turbulence, clouds, and convection in atmospheric models. We focus on cloud transitions that are critical for weather and climate prediction, such as the stratocumulus to cumulus transition over the ocean, and the diurnal cycle of convection (from shallow to deep) over tropical land. We present the latest results from the recent developments and implementations of these unified approaches in climate models, and the impact on climate simulations. We close with a discussion on the potential impact of fully unified turbulence, clouds, and convection parameterizations on climate change projections.

Dr. João Teixeira

João Teixeira is the Atmospheric Infrared Sounder (AIRS) Science Team Leader and the Co- Director of the Center for Climate Sciences at NASA’s Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech). He is also a Visiting Associate and Lecturer at Caltech and a Visiting Scientist at UCLA. Prior to JPL he was at the European Center for Medium-range Weather Forecasts (ECMWF) and the U.S. Naval Research Laboratory (NRL).

He has received the NASA Exceptional Achievement Medal and the NASA Outstanding Public Leadership Medal.

He uses theory, models, and observations to better monitor, understand, and predict the physics of the climate system, focusing on turbulence, clouds, and climate. He is one of the original developers of the Eddy-Diffusivity/Mass-Flux (EDMF) unified parameterization for turbulence and convection. Over the last 20 years, he has been leading the development of increasingly more sophisticated versions of EDMF, that are now able to fully unify boundary layer mixing, shallow and deep convection. Versions of EDMF have been implemented operationally in a variety of weather prediction models. He has developed several other methods to improve the physical and numerical representation of a variety of processes in atmospheric models. In addition, he has developed and used new satellite datasets to better characterize the Earth’s atmosphere. He has served on a variety of national and international committees and currently plays a key role in leading the development of a potential new NASA mission to better observe the planetary boundary layer from space.

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