Understanding mjo dynamics and model bias in dynamo hindcasts
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Understanding MJO dynamics and model bias in DYNAMO hindcasts. Eric D. Maloney, Colorado State University Co ntributors : Walter Hannah, Emily Riley, Adam Sobel , WGNE MJO Task Force We acknowledge: NOAA ESS Program, NSF Climate and Large Scale Dynamics, NASA CYGNSS.

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Understanding MJO dynamics and model bias in DYNAMO hindcasts

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Understanding mjo dynamics and model bias in dynamo hindcasts

Understanding MJO dynamics and model bias in DYNAMO hindcasts

Eric D. Maloney, Colorado State University

Contributors: Walter Hannah, Emily Riley, Adam Sobel, WGNE MJO Task Force

We acknowledge: NOAA ESS Program, NSF Climate and Large Scale Dynamics, NASA CYGNSS

Previous work has hypothesized that the mjo is a moisture mode

Previous Work Has Hypothesized that the MJO is a Moisture Mode

mm day-1

Contour: 4 mm day-1

Maloney et al. (2010),

Sobel and Maloney (2012; 2013)

  • MJO destabilized by cloud-radiation and wind-evaporation feedbacks, and horizontal advection important for eastward propagation.

Have used this hypothesis to explore mjo moistening processes in variety of datasets

Have Used This Hypothesis to Explore MJO Moistening Processes in Variety of Datasets

Partitioning of Column Horizontal MSE Advection in ERA-I

Peak Precip.

Kiranmayi and Maloney (2011, JGR)

  • In the MJO initiation region, MSE (latent heat) anomalies build in anomalous low-level easterlies, and drying occurs in anomalous westerlies.

Importance of surface flux feedbacks for destabilizing the mjo

Importance of Surface Flux Feedbacks for Destabilizing the MJO

Extended Record RAMA Array Surface Fluxes versus Precipitation

Equator, 90oE RAMA Buoy Fluxes

versus TRMM Precip.

Riley and Maloney (2014)

  • Surface flux anomalies average about 10-15% of precipitation anomalies, which from moisture mode theory make them a significant factor in MJO destabilization (MSE sources required to be ~20%).

Understanding mjo dynamics and model bias in dynamo hindcasts

Vertical Structure and Diabatic Processes of

the MJO: Global Model Evaluation Project

GASS and MJO Task Force/YOTC

Time step / 2 –Day

Physics Errors

Daily / Weekly

Forecast Errors

Long-Term Climate

Simulation Errors

1. climate simulation – multi-year simulations coupled or atmosphere only

2.short range hindcasts – daily 48hr lead during ~20 days of the MJO

3.medium range hindcasts – daily 20-day lead time


Analysis leads: Xianan Jiang, Nick Klingaman, Prince Xavier

Mjo dynamics will be explored in dynamo hindcast experiments using moisture mode paradigm

MJO Dynamics Will be Explored in DYNAMO Hindcast Experiments Using Moisture Mode Paradigm


  • 3 configurations were used with differentvalues of entrainment used in the dilute CAPE calculation (e.g. see Klein et al. 2012)

  • Common method to increase MJO activity in models

  • SP-CAM

  • Ocean-Atmosphere-Land Model (OLAM, Walkoand Avissar 2008a,b; Walko and Avissar2011)


    • Initial conditions created from ECMWF operational analysis

    • Simulations lasted 20-days starting every 5th day from 01 Oct – 15 Dec, 2011

  • Common method of improving convective moisture sensitivity turn up entrainment

    Common Method of Improving Convective Moisture Sensitivity: Turn up Entrainment

    Increasing Entrainment Rate

    • NCAR CAM5 One-Week Hindcasts During the DYNAMO period with Variable Entrainment Rates

    Hannah and Maloney (2014)

    Higher entrainment rate better hindcast skill

    Higher Entrainment Rate = Better Hindcast Skill

    Hannah and Maloney (2014)

    High Entrainment

    Low Entrainment

    Lowering the entrainment parameter has a dramatic effect on RMM skill scores

    Bivariate correlation and RMSE versus observed RMMs (e.g. Gottschalk et al. 2010)

    But not all is rosy

    But Not All is Rosy…..

    • We will use the vertically-integrated MSE budget to demonstrate.

    • For weak tropical temperature gradients (WTG):

    • For WTG,

    • Vertically-integrated MSE budget thus becomes a convenient way of diagnosing and modeling MJO dynamics, assuming MJO is regulated by WTG theory and resembles a moisture mode

    s=dry static energy

    m=moist static energy

    LE= Latent heat flux

    SH=sensible heat flux

    R= radiative heating

    Partitioning of mse budget terms incorrect

    Partitioning of MSE Budget Terms Incorrect

    • Vertical MSE advection imports energy on average unlike ERA-I, MSE sources and their variability also too weak


    High Entrainment

    Hannah and Maloney (2014)

    Erroneous partitioning of column mse budgets terms with increased entrainment dynamo array region

    Erroneous Partitioning of Column MSE Budgets Terms with Increased Entrainment (DYNAMO Array Region)

    Vertical advection erroneously

    imports MSE into the column with high entrainment



    May Be Compensating for Too-Weak

    Radiative Feedbacks to Produce Good MJO?

    Hannah and Maloney (2014)

    Reasons for mse budget biases and consequences

    Reasons for MSE Budget Biases and Consequences

    • Higher entrainment simulation of CAM5 have more bottom-heavy vertical velocity and heating profiles than ERA-I (and DYNAMO array), indicating errors in the simulation of parameterized convection

    • Differences in vertical MSE advection relative to ERA-I are thus produced.

    • These MSE budget biases may provide clues as to why improving the MJO in climate models using certain techniques tends to degrade the mean state (e.g. Kim et al. 2011)

    Sp cam produces very robust events

    SP-CAM Produces Very Robust Events

    • Combination of strong SP-CAM events and climate drift that projects onto the RMMs produces outstanding anomaly correlation skill scores but high RMS error




    Hannah and Maloney (2014b)

    U850 Drift

    Sp cam produces more bottom heavy heating profile on average compared to era i

    SP-CAM Produces More Bottom-Heavy Heating Profile on Average Compared to ERA-I

    • Bottom heavy heating (and vertical velocity) profile produces excessive MSE import into the column that makes MJO too strong.

    Stronger convection

    Hannah and Maloney (2014b)

    Understanding mjo dynamics and model bias in dynamo hindcasts

    Ocean Atmosphere Land Model

    • OLAM(Walkoand Avissar 2008a,b; Walko and Avissar2011) has a grid topology that enables local mesh refinement to any degree without the need for special grid nesting algorithms

    • We are currently testing parameterization dependences, nudging and initialization strategies

    • The left figure shows one configuration we have tested with a single mesh refinement.

    • The inner domain will eventually be cloud system resolving

    One grid of refinement with ~100 and 50km outer and inner domains. Inner refined mesh centered 0, 72E

    Understanding mjo dynamics and model bias in dynamo hindcasts

    OLAM Unfiltered Precipitation and Wind

    • OLAM can capture the essence of the first two MJO events with fidelity

    Understanding mjo dynamics and model bias in dynamo hindcasts

    OLAM MSE Anomalies

    • OLAM can capture the essence of the first two MJO events with fidelity



    • We have presented multiple modeling and observational results that use moisture mode theory to understand the basic dynamics of the MJO.

    • We have presented process-oriented diagnostics applied to model hindcast experiments that may help explain why models with good MJO simulations sometimes have degraded mean states.

    • We have shown some initial hindcast experiments with OLAM that are promising in their simulations of DYNAMO MJO events.

    Issues and future work

    Issues and Future Work

    • Simple fixes to improve model MJO simulations may produce good MJO activity for the wrong reasons.

    • In addition to more realistic treatments of entrainment, more emphasis might be placed on mesoscale organization and its impacts, microphysics, and simulation of the continuum of cloud populations and their reflection in vertical heating structure to produce reliable simulations of the MJO (e.g. Chikira 2014)

    • More process-oriented diagnosis of models is needed to assess whether models are producing correct MJO simulations for the right reasons. The OLAM model with its refined mesh capabilities may prove an extremely useful tool in this endeavor.



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