Editorial Type:
Article
Article Type:
Research Article

MJO Simulation Diagnostics

CLIVAR MADDEN–JULIAN OSCILLATION WORKING GROUP: D. WALISER,* Jet Propulsion Laboratory, Pasadena, California; K. SPERBER,* Lawrence Livermore National Laboratory, PCMDI, Livermore,California; H. HENDON, Centre for Australian Weather and Climate Research, Melbourne, Australia; D. KIM, Seoul National University, Seoul, South Korea; E. MALONEY, Colorado State University, Fort Collins, Colorado; M. WHEELER, Centre for Australian Weather and Climate Research, Melbourne, Australia; K. WEICKMANN, NOAA/Earth System Research Laboratory, Boulder, Colorado;C. ZHANG, Rosenstiel School of Marine and Atmospheric Science, Miami, Florida; L. DONNER, NOAA/GFDL, Princeton, New Jersey; J. GOTTSCHALCK, W. HIGGINS, NOAA/NCEP, Camp Springs, Maryland; I.-S. KANG, Seoul National University, Seoul, South Korea; D. LEGLER, U.S. CLIVAR Office, Washington, D.C.; M. MONCRIEFF, NCAR, Boulder, Colorado; S. SCHUBERT, NASAGSFC, Greenbelt, Maryland; W. STERN, NOAA/GFDL, Princeton, New Jersey; F. VITART, European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; B. WANG, IPRC, University of Hawaii at Manoa, Honolulu, Hawaii; W. WANG, NOAA/NCEP, Camp Springs, Maryland; S. WOOLNOUGH, University of Reading, Reading, United Kingdom

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Print Publication:
01 Jun 2009
DOI:
https://doi.org/10.1175/2008JCLI2731.1
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3006–3030
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Abstract

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

Corresponding author address: Duane Waliser, Jet Propulsion Laboratory, MS 183-505, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109. Email: duane.waliser@jpl.nasa.gov

Abstract

The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.

Corresponding author address: Duane Waliser, Jet Propulsion Laboratory, MS 183-505, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109. Email: duane.waliser@jpl.nasa.gov

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