scholarly journals The Madden–Julian Oscillation in CCSM4

2011 ◽  
Vol 24 (24) ◽  
pp. 6261-6282 ◽  
Author(s):  
Aneesh C. Subramanian ◽  
Markus Jochum ◽  
Arthur J. Miller ◽  
Raghu Murtugudde ◽  
Richard B. Neale ◽  
...  
Keyword(s):  
Large Scale ◽  
Intraseasonal Variability ◽  
Power Spectra ◽  
Longwave Radiation ◽  
Meridional Wind ◽  
Dominant Mode ◽  
Madden Julian Oscillation ◽  
Asian Monsoon Region ◽  
Climate System Model ◽  
Zonal Winds

Abstract This study assesses the ability of the Community Climate System Model, version 4 (CCSM4) to represent the Madden–Julian oscillation (MJO), the dominant mode of intraseasonal variability in the tropical atmosphere. The U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group’s prescribed diagnostic tests are used to evaluate the model’s mean state, variance, and wavenumber–frequency characteristics in a 20-yr simulation of the intraseasonal variability in zonal winds at 850 hPa (U850) and 200 hPa (U200), and outgoing longwave radiation (OLR). Unlike its predecessor, CCSM4 reproduces a number of aspects of MJO behavior more realistically. The CCSM4 produces coherent, broadbanded, and energetic patterns in eastward-propagating intraseasonal zonal winds and OLR in the tropical Indian and Pacific Oceans that are generally consistent with MJO characteristics. Strong peaks occur in power spectra and coherence spectra with periods between 20 and 100 days and zonal wavenumbers between 1 and 3. Model MJOs, however, tend to be more broadbanded in frequency than in observations. Broad-scale patterns, as revealed in combined EOFs of U850, U200, and OLR, are remarkably consistent with observations and indicate that large-scale convergence–convection coupling occurs in the simulated MJO. Relations between MJO in the model and its concurrence with other climate states are also explored. MJO activity (defined as the percentage of time the MJO index exceeds 1.5) is enhanced during El Niño events compared to La Niña events, both in the model and observations. MJO activity is increased during periods of anomalously strong negative meridional wind shear in the Asian monsoon region and also during strong negative Indian Ocean zonal mode states, in both the model and observations.

scholarly journals Improved Madden–Julian Oscillations with Improved Physics: The Impact of Modified Convection Parameterizations

2012 ◽  
Vol 25 (4) ◽  
pp. 1116-1136 ◽  
Author(s):  
Lei Zhou ◽  
Richard B. Neale ◽  
Markus Jochum ◽  
Raghu Murtugudde
Keyword(s):  
Coherent Structures ◽  
Large Scale ◽  
Deep Convection ◽  
Intraseasonal Variability ◽  
Conveyor Belt ◽  
Convective Heating ◽  
Climate System Model ◽  
Zonal Winds ◽  
Convective Momentum Transport ◽  
The Impact

Abstract Two modifications are made to the deep convection parameterization in the NCAR Community Climate System Model, version 3 (CCSM3): a dilute plume approximation and an implementation of the convective momentum transport (CMT). These changes lead to significant improvement in the simulated Madden–Julian oscillations (MJOs). With the dilute plume approximation, temperature and convective heating perturbations become more positively correlated. Consequently, more available potential energy is generated and the intraseasonal variability (ISV) becomes stronger. The organization of ISV is also improved, which is manifest in coherent structures between different MJO phases and an improved simulation of the eastward propagation of MJOs with a reasonable eastward speed. The improved propagation can be attributed to a better simulation of the low-level zonal winds due to the inclusion of CMT. The authors posit that the large-scale zonal winds are akin to a selective conveyor belt that facilitates the organization of ISVs into highly coherent structures, which are important features of observed MJOs. The conclusions are supported by two supplementary experiments, which include the dilute plume approximation and CMT separately.

scholarly journals Large-Scale Environmental Characteristics of MJOs that Strengthen and Weaken over the Maritime Continent

2018 ◽  
Vol 31 (14) ◽  
pp. 5731-5748 ◽  
Author(s):  
Casey D. Burleyson ◽  
Samson M. Hagos ◽  
Zhe Feng ◽  
Brandon W. J. Kerns ◽  
Daehyun Kim
Keyword(s):  
Seasonal Cycle ◽  
Large Scale ◽  
Longwave Radiation ◽  
Sea Surface ◽  
Madden Julian Oscillation ◽  
Moist Static Energy ◽  
Maritime Continent ◽  
New Finding ◽  
Field Campaigns ◽  
Static Energy

Abstract The characteristics of Madden–Julian oscillation (MJO) events that strengthen and weaken over the Maritime Continent (MC) are examined. The real-time multivariate MJO (RMM) index is used to assess changes in global MJO amplitude over the MC. The MJO weakens at least twice as often as it strengthens over the MC, with weakening MJOs being twice as likely during El Niño compared to La Niña years and the reverse for strengthening events. MJO weakening shows a pronounced seasonal cycle that has not been previously documented. During the Northern Hemisphere (NH) summer and fall the RMM index can strengthen over the MC. MJOs that approach the MC during the NH winter typically weaken according to the RMM index. This seasonal cycle corresponds to whether the MJO crosses the MC primarily north or south of the equator. Because of the seasonal cycle, weakening MJOs are characterized by positive sea surface temperature and moist-static energy anomalies in the Southern Hemisphere (SH) of the MC compared to strengthening events. Analysis of the outgoing longwave radiation (OLR) MJO index (OMI) shows that MJO precipitation weakens when it crosses the MC along the equator. A possible explanation of this based on previous results is that the MJO encounters more landmasses and taller mountains when crossing along the equator or in the SH. The new finding of a seasonal cycle in MJO weakening over the MC highlights the importance of sampling MJOs throughout the year in future field campaigns designed to study MJO–MC interactions.

scholarly journals Indications for a potential synchronization between the phase evolution of the Madden–Julian oscillation and the solar 27-day cycle

2019 ◽  
Vol 19 (7) ◽  
pp. 4235-4256 ◽  
Author(s):  
Christoph G. Hoffmann ◽  
Christian von Savigny
Keyword(s):  
Time Lag ◽  
Intraseasonal Variability ◽  
Longwave Radiation ◽  
Phase Evolution ◽  
Boreal Winter ◽  
Madden Julian Oscillation ◽  
Atmospheric Conditions ◽  
Quasi Biennial Oscillation ◽  
Open Questions ◽  
Solid Foundation

Abstract. The Madden–Julian oscillation (MJO) is a major source of intraseasonal variability in the troposphere. Recently, studies have indicated that also the solar 27-day variability could cause variability in the troposphere. Furthermore, it has been indicated that both sources could be linked, and particularly that the occurrence of strong MJO events could be modulated by the solar 27-day cycle. In this paper, we analyze whether the temporal evolution of the MJO phases could also be linked to the solar 27-day cycle. We basically count the occurrences of particular MJO phases as a function of time lag after the solar 27-day extrema in about 38 years of MJO data. Furthermore, we develop a quantification approach to measure the strength of such a possible relationship and use this to compare the behavior for different atmospheric conditions and different datasets, among others. The significance of the results is estimated based on different variants of the Monte Carlo approach, which are also compared. We find indications for a synchronization between the MJO phase evolution and the solar 27-day cycle, which are most notable under certain conditions: MJO events with a strength greater than 0.5, during the easterly phase of the quasi-biennial oscillation, and during boreal winter. The MJO appears to cycle through its eight phases within two solar 27-day cycles. The phase relation between the MJO and the solar variation appears to be such that the MJO predominantly transitions from phase 8 to 1 or from phase 4 and 5 during the solar 27-day minimum. These results strongly depend on the MJO index used such that the synchronization is most clearly seen when using univariate indices like the OLR-based MJO index (OMI) in the analysis but can hardly be seen with multivariate indices like the real-time multivariate MJO index (RMM). One possible explanation could be that the synchronization pattern is encoded particularly in the underlying outgoing longwave radiation (OLR) data. A weaker dependence of the results on the underlying solar proxy is also observed but not further investigated. Although we think that these initial indications are already worth noting, we do not claim to unambiguously prove this relationship in the present study, neither in a statistical nor in a causal sense. Instead, we challenge these initial findings ourselves in detail by varying underlying datasets and methods and critically discuss resulting open questions to lay a solid foundation for further research.

scholarly journals The Madden–Julian Oscillation in ECHAM6 and the Introduction of an Objective MJO Metric

2013 ◽  
Vol 26 (10) ◽  
pp. 3241-3257 ◽  
Author(s):  
Traute Crueger ◽  
Bjorn Stevens ◽  
Renate Brokopf
Keyword(s):  
Outgoing Longwave Radiation ◽  
Longwave Radiation ◽  
Circulation System ◽  
Madden Julian Oscillation ◽  
Multivariate Approach ◽  
Convection Scheme ◽  
Convective Rainfall ◽  
Zonal Winds ◽  
Basic Features ◽  
Dynamic Signature

Abstract This study presents a quantitative evaluation of the simulated Madden–Julian oscillation (MJO) in an ensemble of 42 experiments performed with ECHAM6 and previous ECHAM versions. The ECHAM6 experiments differ in their parameter settings, resolution, and whether the atmosphere is coupled to an ocean or not. The analysis concentrates on a few basic features of the MJO, namely, the signatures of convection/precipitation coupled with the circulation system and the eastward propagation strength of outgoing longwave radiation (OLR) and 850- and 200-hPa zonal winds within the MJO-related frequency–wavenumber range. It also examines whether precipitation and OLR show similar signatures in the MJO as simulated by ECHAM. The experiments reveal an MJO, however, to different degrees and in different aspects, so that a sound assessment requires a multivariate approach. In particular, the convective rainfall signatures are decoupled from the dynamic signature of the MJO in the simulations herein, which eventually leads to the introduction of a new MJO diagram and metric that incorporate OLR and the zonal winds in 850 and 200 hPa. The analysis here confirms the importance of the convection scheme: only with the Nordeng modifications to the Tiedtke scheme can realistic MJO features be simulated. High-resolution coupled experiments better represent the MJO as compared to low-resolution AMIP experiments. This is shown to follow from two more general findings, namely, that 1) air–sea interaction mainly increases the convective signature and 2) increased resolution enhances eastward propagation.

scholarly journals Predictability of Midsummer Great Plains Low-Level Jet and Associated Precipitation

2020 ◽  
Vol 35 (1) ◽  
pp. 215-235 ◽  
Author(s):  
Kelsey M. Malloy ◽  
Ben P. Kirtman
Keyword(s):  
Great Plains ◽  
North American ◽  
Large Scale ◽  
Linear Regression Analysis ◽  
Intraseasonal Variability ◽  
Warm Season ◽  
Summer Precipitation ◽  
Climate System Model ◽  
Low Level ◽  
Low Level Jet

Abstract Warm-season precipitation in the U.S. “Corn Belt,” the Great Plains, and the Midwest greatly influences agricultural production and is subject to high interannual and intraseasonal variability. Unfortunately, current seasonal and subseasonal forecasts for summer precipitation have relatively low skill. Therefore, there are ongoing efforts to understand hydroclimate variability targeted at improving predictions, particularly through its primary transporter of moisture: the Great Plains low-level jet (LLJ). This study uses the Community Climate System Model, version 4 (CCSM4), July forecasts, made as part of the North American Multi-Model Ensemble (NMME), to assess skill in reproducing the monthly Great Plains LLJ and associated precipitation. Generally, the CCSM4 forecasts capture the climatological jet but have problems representing the observed variability beyond two weeks. In addition, there are predictors associated with the large-scale variability identified through linear regression analysis, shifts in kernel density estimators, and case study analysis that suggest potential for improving confidence in forecasts. In this study, a strengthened Caribbean LLJ, negative Pacific–North American (PNA) teleconnection, El Niño, and a negative Atlantic multidecadal oscillation each have a relatively strong and consistent relationship with a strengthened Great Plains LLJ. The circulation predictors, the Caribbean LLJ and PNA, present the greatest “forecast of opportunity” for considering and assigning confidence in monthly forecasts.

On the predictability of the Madden-Julian Oscillation phase 

2021 ◽  
Author(s):  
Riccardo Silini ◽  
Cristina Masoller ◽  
Marcelo Barreiro
Keyword(s):  
Time Scale ◽  
Short Term Memory ◽  
Heat Waves ◽  
Arima Model ◽  
Longwave Radiation ◽  
Climate Extremes ◽  
Dominant Mode ◽  
Madden Julian Oscillation ◽  
Weather Extremes ◽  
Oscillation Phase

<p>Climate extremes such as heat waves, drought, extreme precipitation or cold surges have huge social and economic impacts that are expected to increase with climate change. Forecasting of such extreme events on the sub-seasonal time scale (from 10 days to about 3 months) is very challenging because of the poor understanding of phenomena that may increase predictability at this time scale. The Madden-Julian Oscillation (MJO) is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales and can also promote or enhance phenomena such as monsoons and hurricanes in other regions of the world. It is a hierarchically organized structure that propagates across the planet with a period of 30 to 60 days, and its phase represents an important source of sub-seasonal predictability. For this reason, forecasting the MJO phase can improve the predictability of weather extremes. Here we use the index of the MJO based on outgoing longwave radiation (OLR), namely the OLR MJO Index (OMI), which is a popular index used for defining MJO phases. We used the first two principal components to compute the MJO phase and amplitude. With an autoregressive integrated moving average (ARIMA) model we found that winter and summer are slightly more predictable than spring and autumn. We also computed the likelihood of having a warm/cold spell during a given MJO phase. For warm spells, we found that the significantly most likely phase is the 7, and the top three are 7, 8 and 1, which are, as expected, consecutive phases. For cold spells, phases 5 and 1 play important roles, while phase 3 is by far the least likely to have cold spells. Ongoing work is devoted to compare the skill of neural network approaches (long-short term memory, LSTM, and gated recurrent unit, GRU) for the prediction of the MJO phases and warm/cold spells. Acknowledgment: work funded by ITN CAFE.</p>

scholarly journals Seasonal and Intraseasonal Variability of Mesoscale Convective Systems over the South Asian Monsoon Region

2016 ◽  
Vol 73 (12) ◽  
pp. 4753-4774 ◽  
Author(s):  
Katrina S. Virts ◽  
Robert A. Houze
Keyword(s):  
South Asian ◽  
Bay Of Bengal ◽  
Large Scale ◽  
Intraseasonal Variability ◽  
Intraseasonal Oscillation ◽  
Mesoscale Convective Systems ◽  
Asian Monsoon Region ◽  
Convective Systems ◽  
Mesoscale Convective ◽  
Monsoon Depressions

Abstract Seasonal and intraseasonal differences in mesoscale convective systems (MCSs) over South Asia are examined using A-Train satellites, a ground-based lightning network, and reanalysis fields. Premonsoon (April–May) MCSs occur primarily over Bangladesh and the eastern Bay of Bengal. During the monsoon (June–September), small MCSs occur over the Meghalaya Plateau and northeast Himalayan notch, while large and connected MCSs are most widespread over the Bay of Bengal. Monsoon MCSs produce less lightning and exhibit more extensive stratiform and anvil reflectivity structures in CloudSat observations than do premonsoon MCSs. During the monsoon, Bay of Bengal and Meghalaya Plateau MCSs vary with the 30–60-day northward-propagating intraseasonal oscillation, while northeast Himalayan notch MCSs are associated with weak large-scale anomalies but locally enhanced CAPE. During intraseasonal active periods, a zone of enhanced large and connected MCSs, precipitation, and lightning extends from the northeastern Arabian Sea southeastward over India and the Bay of Bengal, flanked by suppressed anomalies. Spatial variability is observed within this enhancement zone: lightning is most enhanced where MCSs are less enhanced, and vice versa. Reanalysis composites indicate that Bay of Bengal MCSs are associated with monsoon depressions, which are frequent during active monsoon periods, while Meghalaya Plateau MCSs are most frequent at the end of break periods, as anomalous southwesterly winds strengthen moist advection toward the terrain. Over both regions, MCSs exhibit more extensive stratiform and anvil regions and less lightning when the large-scale environment is moister, and vice versa.

scholarly journals A Revised Real-Time Multivariate MJO Index

2016 ◽  
Vol 144 (2) ◽  
pp. 627-642 ◽  
Author(s):  
Ping Liu ◽  
Qin Zhang ◽  
Chidong Zhang ◽  
Yuejian Zhu ◽  
Marat Khairoutdinov ◽  
...  
Keyword(s):  
Real Time ◽  
Covariance Matrix ◽  
Correlation Matrix ◽  
Dominant Role ◽  
Power Spectra ◽  
Madden Julian Oscillation ◽  
Convective Initiation ◽  
Zonal Winds ◽  
Simple Scaling ◽  
The Tropics

Abstract This study investigates why OLR plays a small role in the Real-time Multivariate (Madden–Julian oscillation) MJO (RMM) index and how to improve it. The RMM index consists of the first two leading principal components (PCs) of a covariance matrix, which is constructed by combined daily anomalies of OLR and zonal winds at 850 (U850) and 200 hPa (U200) in the tropics after being normalized with their globally averaged standard deviations of 15.3 W m−2, 1.8 m s−1, and 4.9 m s−1, respectively. This covariance matrix is reasoned mathematically close to a correlation matrix. Both matrices substantially suppress the overall contribution of OLR and make the index more dynamical and nearly transparent to the convective initiation of the MJO. A covariance matrix that does not use normalized anomalies leads to the other extreme where OLR plays a dominant role while U850 and U200 are minor. Numerous tests indicate that a simple scaling of the anomalies (i.e., 2 W m−2, 1 m s−1, and 1 m s−1) can better balance the roles of OLR and winds. The revised PCs substantially enhance OLR over the eastern Indian and western Pacific Oceans and change it less notably in other locations, while they reduce U850 and U200 only slightly. Comparisons with the original RMM in spatial structure, power spectra, and standard deviation demonstrate improvements of the revised RMM index.

scholarly journals Centered Composite Analysis of Variations Associated with the Madden–Julian Oscillation

2006 ◽  
Vol 19 (9) ◽  
pp. 1834-1849 ◽  
Author(s):  
Bryan C. Weare
Keyword(s):  
Surface Pressure ◽  
Lower Troposphere ◽  
Longwave Radiation ◽  
Specific Humidity ◽  
Composite Analysis ◽  
Madden Julian Oscillation ◽  
Mixing Properties ◽  
Zonal Winds ◽  
The Indian Ocean ◽  
Key Events

Abstract Centered composite analysis is described and applied to gain a better understanding of the initial phases of the Madden–Julian oscillation (MJO). Centered composite analysis identifies the dates and central locations of key events. The elements of the composite means are centered on these central locations before averages are calculated. In this way much of the spatial fuzziness, which is inherent in traditional composite analysis, is removed. The results for the MJO, based on MJO-filtered outgoing longwave radiation for the reference data and 40-yr ECMWF Re-Analysis (ERA-40) and NCEP–NCAR reanalysis products for the composites, show highly significant composites of unfiltered data for not only zero lag, but also lags back to 20 days before the target events. These composites identify propagating patterns of surface pressure, upper- and lower-troposphere zonal winds, surface temperature, and 850-hPa specific humidity associated with MJO convective events in the Indian Ocean. The propagation characteristics of important features, especially surface pressure, differ substantially for MJO convective anomalies centered over the Indian or western Pacific Oceans. This suggests that distinctly different mechanisms may be dominant in these two regions, and that many earlier analyses may be mixing properties of the two.

scholarly journals Intraseasonal Variability and Possible Causes of Large Scale and Convective Precipitations Over the Gangetic Plain of India

2021 ◽  
Author(s):  
Pradhan Parth Sarthi ◽  
Praveen Kumar
Keyword(s):  
Summer Monsoon ◽  
Large Scale ◽  
Monsoon Rainfall ◽  
Intraseasonal Variability ◽  
Temporal Distribution ◽  
India Meteorological Department ◽  
Longwave Radiation ◽  
Summer Monsoon Rainfall ◽  
Convective Precipitation ◽  
Gangetic Plain

Abstract In India, summer monsoon rainfall during June-July-August-September (JJAS) along the river Ganga is the lifeline. Since its variability predominantly affects the agriculture production, drought and flood over the densely populated meteorological subdivisions of the Gangetic West Bengal, Jharkhand, Bihar, East and West Uttar Pradesh. Owing to its importance, a large number of research on the variability of Indian Summer Monsoon Rainfall (ISMR) has been conducted. However, the types of rainfall (or precipitation), i.e. Large Scale Precipitation (LSP) and Convective Precipitation (CP), is less discussed. The LSP is precipitated out from the stratus or nimbostratus clouds, while CP occurs from the cumulus and cumulonimbus clouds, and both of them coexists during summer monsoon months. The current research aims to know the climatological characteristics and possible cause of occurrence of these two types of precipitation over the meteorological subdivisions. For this purpose, the data of LSP, CP, zonal, meridonal (u and v component) wind and Relative Humidity (RH) at the spatial resolution of 0.25° x 0.25° (25km) for the period of 1980-2019 are taken from the European Centre for Medium-Range Weather Forecasts (ECMWF), UK. The Outgoing Longwave Radiation (OLR) data at a surface resolution of 1° x 1° for the same months and periods are obtained from the National Centre for Environmental Information (NOAA), USA. The observed rainfall data of the India Meteorological Department (IMD) at the same resolution and period is considered and compared with ERA data. The spatial and temporal distribution of both types of precipitation is analyzed as well as their linkage with OLR, zonal winds and RH at pressure levels of 1000, 850 and 700hPa is examined.

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