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 MJO destabilized by cloud-radiation and wind-evaporation feedbacks, and horizontal advection important for eastward propagation. Maloney et al. (2010), Sobel and Maloney (2012; 2013) mm day -1 Contour: 4 mm day -1

Have Used This Hypothesis to Explore MJO Moistening Processes in Variety of Datasets In the MJO initiation region, MSE (latent heat) anomalies build in anomalous low-level easterlies, and drying occurs in anomalous westerlies. Kiranmayi and Maloney (2011, JGR) Partitioning of Column Horizontal MSE Advection in ERA-I Peak Precip.

Importance of Surface Flux Feedbacks for Destabilizing the MJO 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%). Extended Record RAMA Array Surface Fluxes versus Precipitation Equator, 90 o E RAMA Buoy Fluxes versus TRMM Precip. Riley and Maloney (2014)

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 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 Analysis leads: Xianan Jiang, Nick Klingaman, Prince Xavier

MJO Dynamics Will be Explored in DYNAMO Hindcast Experiments Using Moisture Mode Paradigm - NCAR CAM5 3 configurations were used with different values 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, Walko and Avissar 2008a,b; Walko and Avissar 2011) Hindcasts – Initial conditions created from ECMWF operational analysis – Simulations lasted 20-days starting every 5 th day from 01 Oct – 15 Dec, 2011

Common Method of Improving Convective Moisture Sensitivity: Turn up Entrainment Hannah and Maloney (2014) NCAR CAM5 One-Week Hindcasts During the DYNAMO period with Variable Entrainment Rates  Increasing Entrainment Rate

Lowering the entrainment parameter has a dramatic effect on RMM skill scores High Entrainment Bivariate correlation and RMSE versus observed RMMs (e.g. Gottschalk et al. 2010) Low Entrainment Higher Entrainment Rate = Better Hindcast Skill Hannah and Maloney (2014)

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 9 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 Hannah and Maloney (2014) Vertical MSE advection imports energy on average unlike ERA-I, MSE sources and their variability also too weak High Entrainment ERA-I

Erroneous Partitioning of Column MSE Budgets Terms with Increased Entrainment (DYNAMO Array Region) Hannah and Maloney (2014) Vertical advection erroneously imports MSE into the column with high entrainment May Be Compensating for Too-Weak Radiative Feedbacks to Produce Good MJO? ERA-I Models

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 Hannah and Maloney (2014b) Combination of strong SP-CAM events and climate drift that projects onto the RMMs produces outstanding anomaly correlation skill scores but high RMS error One-Week Forecast, U850 U850 Drift

SP-CAM Produces More Bottom-Heavy Heating Profile on Average Compared to ERA-I Hannah and Maloney (2014b) Bottom heavy heating (and vertical velocity) profile produces excessive MSE import into the column that makes MJO too strong. Stronger convection

Ocean Atmosphere Land Model OLAM (Walko and Avissar 2008a,b; Walko and Avissar 2011) 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

OLAM Unfiltered Precipitation and Wind OLAM can capture the essence of the first two MJO events with fidelity

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

Summary 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 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.

Thanks!