Process-oriented MJO Simulation Diagnostic: Moisture Sensitivity of Simulated Convection Daehyun Kim 1, Prince Xavier 2, Eric Maloney 3, Matthew Wheeler.

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Process-oriented MJO Simulation Diagnostic: Moisture Sensitivity of Simulated Convection Daehyun Kim 1, Prince Xavier 2, Eric Maloney 3, Matthew Wheeler 4, Duane Waliser 5, Kenneth Sperber 6, Harry Hendon 4, Chidong Zhang 7, Richard Neale 8, Yen-Ting Hwang 9, and Haibo Liu 10 on behalf of the WMO WGNE MJO Task Force 1 University of Washington, 2 Met Office Hadley Centre, 3 Colorado State University, 4 Centre for Australian Weather and Climate Research, 5 Jet Propulsion Laboratory, 6 Lawrence Livermore National Laboratory, 7 Rosenstiel School of Marine and Atmospheric Science, 8 National Center for Atmospheric Research, 9 National Taiwan University, 10 Lamont-Doherty Earth Observatory WWOSC2014, Montréal August 20, 2014

ULTIMATE QUESTIONS Why does the MJO exist in nature? What are key processes of the MJO?

MJO SIMULATION CAPABILITY East/West= A/B Space-time power spectrum (GPCP precipitation) A B CMIP3 models n=17, avg=1.86 East/West CMIP5 models n=29, avg=1.77 East/West

ROLE OF CUMULUS PARAMETERIZATION MJO Variance (eastward wavenumber 1-6, periods 30-70days) SNUAGCM different convection schemes (Lin et al. 2008) (Lin et al. 2006) CMIP3 models CMIP5 models (Hung et al. 2013)

MJO – mean state trade off JJA mean precipitation Observations :GPCP worse-MJO models better-MJO models Kim et al. (2011) 10 AGCM simulations

NEED FOR A DIAGNOSTIC Hello, how good my MJO is? You don’t have an MJO. What?? What should I do? Well, don’t worry too much. I have seen many GCMs like you. It relieves me, but I really want to have a decent MJO. It’s not easy, but let’s see if I could provide you some advices. A GCM Dr. Kim

MJO Moisture-convection coupling Wind-induced surface heat exchange Cloud-radiation interaction Frictional wave- CISK Cumulus convection Cloud microphysics Radiation Boundary layer Unresolved scale process Resolved scale process Target phenomenon Relatively easy to constrain Reflects characteristics of unresolved scale processes

NEED FOR A DIAGNOSTIC 1.that represents certain features of resolved-scale process that are important in the MJO dynamics Moisture-convection coupling 2.that provides insights into characteristics of unresolved- scale process (i.e. parameterizations) in GCMs Cumulus parameterization MJO Target phenomenon Moisture-convection coupling Resolved-scale process Cumulus convection Unresolved-scale process

MOISTURE-CONVECTION COUPLING Precipitable water (mm) Precipitation (mm day -1 ) Bretherton et al. (2004) Mean daily precipitation in 1mm bin PW (SSM/I) Tropical convection is strongly coupled to tropospheric water vapor

MJO MOISTURE-CONVECTION COUPLING Space-time coherence spectrum (Precipitation & PW) Yasunaga and Mapes (2012) MJO is distinguished from other waves by the strong coupling between moisture and convection anomalies Kelvin Equatorial Rossby Westward inertio-gravity Eastward inertio-gravity MJO

STRATEGY 1.Develop a diagnostic/metric that represents the moisture-convection coupling 2.Apply it to model simulation data and obtain one number from each model (14 CMIP CMIP5) 3.Examine the relationship between the moisture- sensitivity metric and MJO simulation fidelity

RH-PRCP diagnostic Thayer-Calder and Randall (2009) worse-MJO version better-MJO version Relative humidity (%) averaged at different rain rate bins Reanalysis /observations

STRENGTH OF CONVECTION Same 20 mm day -1 rain event means different strength in different models Precipitation amount (mm day -1 ) corresponding to precipitation percentiles (3-year daily data, 10 o S-10 o N, o E)

MOISTURE SENSITIVITY METRIC Moisture sensitivity RH upper X% - RH lower Y% RH: average of 850 and 700 hPa values 850 hPa relative humidity (%) corresponding to precipitation percentiles (3-year daily data, 10 o S-10 o N, o E)

MOISTURE SENSITIVITY OF CONVECTION VS. MJO SIMULATION FIDELITY East/West RH upper 10% - RH lower 20% R 2 = 0.56 (0.65) Estimates from observations/reanalysis

SUMMARY & CONCLUSIONS The robust statistical relationship between moisture sensitivity of convection and MJO simulation fidelity suggests that moisture-convection relationship is an important resolved process for GCMs to simulate a reasonable MJO. There are several aspects of cumulus parameterization (e.g. fractional entrainment rate, rain re-evaporation) that are known to affect the moisture- convection relationship. Further studies are required to reveal effects of specific parameterization changes on the moisture sensitivity metric in each model. There are uncertainties in observational estimates. Relative humidity from reanalysis products is affected by parameterizations of the model used in assimilation systems. The rain products underestimate the frequency of light rain events.

ANOTHER MODEL GROUP East/West RH upper 5% - RH lower 10% Courtesy of Xianan Jiang (JPL)

ROBUSTNESS OF THE RELATIONSHIP RH upper X% - RH lower Y% Mean of correlations between three MJO fidelity metrics and moisture sensitivity metric as a function of X and Y

SENSITIVITY TO DATA PRODUCTS

HOW TO MAKE IT BETTER