Role of water vapor and convection‐circulation decoupling in MJO simulations by a tropical channel model

Ulate, Marcela; Zhang, Chidong; Dudhia, Jimy

doi:10.1002/2014ms000393

Cited by 15 publications

(11 citation statements)

References 95 publications

(152 reference statements)

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“…They can reproduce the eastward propagating signals in zonal wind [ Gustafson and Weare , , ; Ray et al ., ]. But they fail to reproduce the corresponding signals in rainfall using a variety of cumulus and boundary layer parameterization schemes [ Ulate et al ., ] without brute force nudging of model humidity toward that from global reanalysis products [ Hagos et al ., ; Ulate et al ., ] or prescribing high‐frequency sea surface temperature (SST) with intraseasonal fluctuations [ Hagos and Leung , ; Wang et al ., ]. This has led to a hypothesis that cumulus parameterization may act to degrade MJO signals if not producing them and a conclusion that convection and the circulation of the MJO can be decoupled in a regional model [ Ulate et al ., ], which may occur also in global models [ Crueger et al ., ].…”

Section: Introductionmentioning

confidence: 99%

Roles of deep and shallow convection and microphysics in the MJO simulated by the Model for Prediction Across Scales

2016

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The November event of the Madden-Julian oscillation (MJO) during the Dynamics of North Atlantic Models (DYNAMO) field campaign was simulated using the global compressible nonhydrostatic Model for Prediction Across Scales with global coarse (60 and 15 km) and regional (the Indian Ocean) cloud-permitting (3 km) meshes. The purpose of this study is to compare roles of parameterized deep and shallow cumulus and microphysics in MJO simulations. Two cumulus schemes were used: Tiedtke and Grell-Freitas. The deep and shallow components of Tiedtke scheme can be turned on and off individually. The results reveal that microphysics alone (without cumulus parameterization) is able to produce strong signals of the MJO in precipitation with 3 km mesh and weak MJO signals with 15 km mesh. A shallow scheme (Tiedtke) along with microphysics strengthens the MJO signals but makes them less well organized on large scales. A deep cumulus scheme can either improve the large-scale organization of MJO precipitation produced by microphysics and shallow convection (Tiedtke) or impair them (Grell-Freitas). The deep scheme of Tiedtke cannot reproduce the MJO well without its shallow counterpart. The main role of shallow convection in the model is to transport moisture upward to the lower to middle troposphere. By doing so, it removes dry biases in the lower to middle troposphere, a distinct feature in simulations with weak or no MJO signals, and enhances total precipitation and diabatic heating produced by microphysics and deep cumulus schemes. Changing model grid spacing from 60 to 15 km makes a little difference in the model fidelity of reproducing the MJO. All roles of shallow convection in 15 km simulations with parameterized deep convection cannot be reproduced in 3 km simulations without parameterized deep convection. Results from this study suggest that we should pay more attention to the treatment of shallow convection and its connection to other parameterized processes for improving MJO simulations. In other words, a holistic approach should be taken that consider parameterization of shallow cumulus, microphysics, boundary layer, and deep cumulus as a whole for model improvement.Many studies of modeling comparisons have attempted to identity causes of model infidelity in simulating the MJO [Lin et al., 2006;Zhang et al., 2006;Kim et al., 2009]. None of them has led to any definitive answer. Recent studies diagnosed reproduction of the MJO in three types of global model runs: 20 year climate simulations [Jiang et al., 2015], 20 day hindcast [Klingaman et al., 2015a], and 2 day hindcast [Xavier et al., 2015]. Their finding is intriguing: There is no one-to-one correspondence between model success or failure in producing MJO statistics in 20 year runs and MJO hindcast of 20 and 2 days, there is no clear indication of what biases in the mean state might be responsible for model failure of reproducing the MJO, and there is PILON ET AL. DEEP AND SHALLOW CONVECTION IN THE MJO 10,575

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Section: Introductionmentioning

confidence: 99%

Roles of deep and shallow convection and microphysics in the MJO simulated by the Model for Prediction Across Scales

2016

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“…This feature evidenced in observations is sometimes described as the MJO signal "leaping ahead" of the main MJO envelope (Peatman et al 2014). The good performance of WRF here is reflective of the importance of having the right meanstate water vapour distribution, as highlighted by Ulate et al (2015), and of accounting for the high-frequency variability in the SSTs, as noted in Stan (2017), through the use of the SSTs from CFSR. A comparison of Fig.…”

Section: Evidence Of Successful Iso Downscalingmentioning

confidence: 69%

“…The former is needed to correct the WRF model's tendency to overly moisten the atmosphere as a result of excessive surface evaporation in the subtropics (related to problems in low-cloud parameterization), whereas the latter is necessary for the model to capture the Quasi-Biennial Oscillation (QBO; Andrews et al 1987;Saha et al 2010). A good simulation of q v is necessary for the model to properly simulate the MJO (Hagos et al 2011;Ulate et al 2015). The nudging timescale is uniformly equal to 1 h. In a paper currently under preparation the sensitivity of WRF's dynamical and thermodynamic fields to the nudging formulation in the tropics is investigated.…”

Section: Numerical Modelmentioning

confidence: 99%

“…In that work WRF was run for 5 years with a horizontal grid spacing of 36 km and with different configurations and it was found that in the RCM the MJO was no better simulated than in coarse resolution GCMs mainly due to an error in the mean state. More recently Ulate et al (2015) stressed the importance of capturing the water vapour distribution for the model to simulate well the MJO. The need to get the moisture correct is also highlighted in Hagos et al (2011).…”

Section: Introductionmentioning

confidence: 99%

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Multi-scale interactions in a high-resolution tropical-belt experiment and observations

2018

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The Weather Research and Forecasting (WRF) model is used to dynamically downscale 27 years of the Climate Forecast System Reanalysis (CFSR) in a tropical belt configuration at 36 km horizontal grid spacing. WRF is found to give a good rainfall climatology as observed by the Tropical Rainfall Measuring Mission (TRMM) and to reproduce well the largescale circulation and surface radiation fluxes. The impact of conventional and Modoki-type El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are confirmed by linear regression. Madden-Julian Oscillation (MJO) and Boreal Summer Intra-seasonal Oscillation (BSISO) are also well-simulated. The WRF simulation shows that conventional El Niño increases (La Niña decreases) the MJO amplitude in the boreal summer while Modoki-type ENSO and IOD impacts are MJO-phase dependent. While WRF is found to perform well on seasonal to sub-seasonal timescales, it does not capture well the diurnal cycle of precipitation over the Maritime Continent. For the investigation of multi-scale interactions through the local diurnal cycle, TRMM data is used instead. In the Maritime Continent, moderate El Niño and La Niña causes antisymmetric enhancement/reduction of the MJO's influence on the diurnal cycle amplitudes with little change in the diurnal phase. Non-linear impacts on the diurnal amplitude with changes in diurnal phase manifest during strong ENSO. Given that the simulation does not employ data assimilation, this modified version of WRF submitted to the model developers is a suitable downscaling tool of CFSR for sub-seasonal to seasonal tropical atmospheric research.

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“…The 10% weakest‐amplitude MJO disturbances, as defined by

{\overset{P}{true}}_{max}

, are discarded. Variations of this MJO identification and tracking technique have been successfully used in several recent studies [ Subramanian and Zhang , ; Ulate et al ., ].…”

Section: Data Model Setup and Methodsmentioning

confidence: 99%

Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM

Benedict

Pritchard

Collins

2015

J Adv Model Earth Syst

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The superparameterized Community Atmosphere Model (SPCAM) is used to investigate the impact and geographic sensitivity of positive Indian Ocean Dipole (1IOD) sea-surface temperatures (SSTs) on Madden-Julian oscillation (MJO) propagation. The goal is to clarify potentially appreciable 1IOD effects on MJO dynamics detected in prior studies by using a global model with explicit convection representation. Prescribed climatological October SSTs and variants of the SST distribution from October 2006, a 1IOD event, force the model. Modest MJO convection weakening over the Maritime Continent occurs when either climatological SSTs, or 1IOD SST anomalies restricted to the Indian Ocean, are applied. However, severe MJO weakening occurs when either 1IOD SST anomalies are applied globally or restricted to the equatorial Pacific. MJO disruption is associated with time-mean changes in the zonal wind profile and lower moist static energy (MSE) in subsiding air masses imported from the Subtropics by Rossby-like gyres. On intraseasonal scales, MJO disruption arises from significantly smaller MSE accumulation, weaker meridional advective moistening, and overactive submonthly eddies that mix drier subtropical air into the path of MJO convection. These results (1) demonstrate that SPCAM reproduces observed time-mean and intraseasonal changes during 1IOD episodes, (2) reaffirm the role that submonthly eddies play in MJO propagation and show that such multiscale interactions are sensitive to interannual SST states, and (3) suggest that boreal fall 1IOD SSTs local to the Indian Ocean have a significantly smaller impact on Maritime Continent MJO propagation compared to contemporaneous Pacific SST anomalies which, for October 2006, resemble El Niño-like conditions.

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Role of water vapor and convection‐circulation decoupling in MJO simulations by a tropical channel model

Cited by 15 publications

References 95 publications

Roles of deep and shallow convection and microphysics in the MJO simulated by the Model for Prediction Across Scales

Roles of deep and shallow convection and microphysics in the MJO simulated by the Model for Prediction Across Scales

Multi-scale interactions in a high-resolution tropical-belt experiment and observations

Sensitivity of MJO propagation to a robust positive Indian Ocean dipole event in the superparameterized CAM

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