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Article Type:
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Process-Oriented MJO Simulation Diagnostic: Moisture Sensitivity of Simulated Convection

Daehyun Kim Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Prince Xavier Met Office Hadley Centre, Exeter, United Kingdom

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Eric Maloney Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Matthew Wheeler Centre for Australian Weather and Climate Research, Melbourne, Australia

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Duane Waliser Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Kenneth Sperber Lawrence Livermore National Laboratory, PCMDI, Livermore, California

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Harry Hendon Centre for Australian Weather and Climate Research, Melbourne, Australia

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Chidong Zhang Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

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Richard Neale National Center for Atmospheric Research, Boulder, Colorado

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Yen-Ting Hwang Department of Atmospheric Sciences, University of Washington, Seattle, Washington

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Haibo Liu Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Print Publication:
15 Jul 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00497.1
Page(s):
5379–5395
Restricted access

Abstract

Process-oriented diagnostics for Madden–Julian oscillation (MJO) simulations are being developed to facilitate improvements in the representation of the MJO in weather and climate models. These process-oriented diagnostics are intended to provide insights into how parameterizations of physical processes in climate models should be improved for a better MJO simulation. This paper proposes one such process-oriented diagnostic, which is designed to represent sensitivity of simulated convection to environmental moisture: composites of a relative humidity (RH) profile based on precipitation percentiles. The ability of the RH composite diagnostic to represent the diversity of MJO simulation skill is demonstrated using a group of climate model simulations participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). A set of scalar process metrics that captures the key physical attributes of the RH diagnostic is derived and their statistical relationship with indices that quantify the fidelity of the MJO simulation is tested. It is found that a process metric that represents the amount of lower-tropospheric humidity increase required for a transition from weak to strong rain regimes has a robust statistical relationship with MJO simulation skill. The results herein suggest that moisture sensitivity of convection is closely related to a GCM’s ability to simulate the MJO.

Current affiliation: Department of Atmospheric Sciences, University of Washington, Seattle, Washington.

Current affiliation: Climate, Atmospheric Science, and Physical Oceanography, Scripps Institute of Oceanography, University of California San Diego, La Jolla, California.

Corresponding author address: Daehyun Kim, Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY 10964-8000. E-mail: dkim@ldeo.columbia.edu

Abstract

Process-oriented diagnostics for Madden–Julian oscillation (MJO) simulations are being developed to facilitate improvements in the representation of the MJO in weather and climate models. These process-oriented diagnostics are intended to provide insights into how parameterizations of physical processes in climate models should be improved for a better MJO simulation. This paper proposes one such process-oriented diagnostic, which is designed to represent sensitivity of simulated convection to environmental moisture: composites of a relative humidity (RH) profile based on precipitation percentiles. The ability of the RH composite diagnostic to represent the diversity of MJO simulation skill is demonstrated using a group of climate model simulations participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). A set of scalar process metrics that captures the key physical attributes of the RH diagnostic is derived and their statistical relationship with indices that quantify the fidelity of the MJO simulation is tested. It is found that a process metric that represents the amount of lower-tropospheric humidity increase required for a transition from weak to strong rain regimes has a robust statistical relationship with MJO simulation skill. The results herein suggest that moisture sensitivity of convection is closely related to a GCM’s ability to simulate the MJO.

Current affiliation: Department of Atmospheric Sciences, University of Washington, Seattle, Washington.

Current affiliation: Climate, Atmospheric Science, and Physical Oceanography, Scripps Institute of Oceanography, University of California San Diego, La Jolla, California.

Corresponding author address: Daehyun Kim, Lamont-Doherty Earth Observatory, Columbia University, 61 Route 9W, Palisades, NY 10964-8000. E-mail: dkim@ldeo.columbia.edu
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