Assessment of Cloud Development and Organization Processes within the Madden-Julian Oscillation using ARM Observations and Lagrangian Modeling

Abstract

Tropical clouds and precipitation play fundamental roles in the global energy and hydrological cycle. Hence, an accurate representation and understanding of cloud properties and processes are key toward improving the skill of climate models. Current global models often struggle to capture the observed characteristics of tropical convective variability due to the uncertainty in the interactive processes between cloud populations and the large-scale dynamics of the atmosphere and ocean. One of the phenomena whose fundamental dynamics has been suggested to involve such interactions is the Madden-Julian Oscillation (MJO). The MJO is the dominant mode of intraseasonal variability in tropical convection and circulation, which influences a wide range of global weather and climate. The large-scale dynamic and thermodynamic perturbations of the MJO influence cloud populations and its degree of organization, while the associated variability in radiative and latent heating feedbacks of clouds may destabilize the MJO. To improve our understanding of this key interdependence between clouds and large-scale perturbations associated with the MJO, this proposed research project aims to achieve a detailed understanding of cloud development and organization processes within the MJO using observations and modeling.

The combined use of observations and modeling will allow us to assess the accuracy of model simulations while taking advantage of numerical experiments to achieve process-level understanding of cloud development and organization mechanisms. This proposed research will focus on the period and domain of the Dynamics of the MJO (DYNAMO)/ARM MJO investigation Experiment (AMIE) field campaign, which provides a unique set of in-situ and remote sensing data of cloud and precipitation properties over the Indian Ocean, including the ARM facility that was deployed over Gan Island. Observational component of this study will mainly use ground-based radar data collected during the field campaign, combined with radiosonde, reanalysis, and satellite data to quantify the observed degree of cloud organization and large-scale environmental factors associated with the MJO. The modeling component will use a hierarchy of numerical model experiments, including Lagrangian Particle Dispersion Model (LPDM) to examine the dynamics of each cloud systems such as updrafts, downdrafts, and cold pools to understand their triggering and organization mechanism at different stages of the MJO. The potential impacts of this proposed research project include: 1) an improved understanding of cloud properties and processes associated with the MJO, 2) identification of consistent metrics to measure the degree of cloud organization both within observation and model, and 3) providing insights into the methods to represent cloud organization processes in global climate models.