MJO simulation in CMIP5 climate models: MJO skill metrics and process-oriented diagnosis

Abstract

coherence between eastward propagation of precipitation/ convection and the wind field. The RHCP-metric, indicative of the sensitivity of simulated convection to low-level environmental moisture, and the NGMS-metric, indicative of the efficiency of a convective atmosphere for exporting moist static energy out of the column, show robust correlations with a large number of MJO skill metrics. The GEFmetric, indicative of the strength of the column-integrated longwave radiative heating due to cloud-radiation interaction, is also correlated with the MJO skill metrics, but shows relatively lower correlations compared to the RHCPand NGMS-metrics. Our results suggest that modifications to processes associated with moisture-convection coupling and the gross moist stability might be the most fruitful for improving simulations of the MJO. Though the GEF-metric exhibits lower correlations with the MJO skill metrics, the longwave radiation feedback is highly relevant for simulating the weak precipitation anomaly regime that may be important for the establishment of shallow convection and the transition to deep convection.

Observed and projected intra‐seasonal variability of Australian monsoon rainfall

“…It is dimensionless. Most CMIP5 models underestimate MJO amplitude, especially when OLR is used in the evaluation, and exhibit too fast phase speed, while lacking coherence between eastward propagation of precipitation/convection and the wind field (Ahn et al ., ). We identified a small group of CMIP5 models which showed the highest skill along these two metrics and Table then identifies these seven models which were diagnosed with the most realistic MJO‐like features.…”

“…While most CMIP5 models underestimate MJO amplitude and exhibit too fast phase speed while lacking coherence between eastward propagation of precipitation/convection and the wind field (Ahn et al ., ), our evaluation revealed a small group of CMIP5 models with above average skill along two MJO metrics (see Figures S1–S4). A separate analysis of these more skilful models reveals significant differences in projected changes of the monsoon metrics between the more skilful models and a similar size group of least skilful models.…”

Validity of parameter optimization in improving MJO simulation and prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

“…Many studies showed that state-of-the-art climate models are capable of reproducing MJO's basic characteristics in intensity, spectrum, spatial structure and propagation as well as the relevant dynamics (e.g., Kim et al 2009Kim et al , 2014bHung et al 2013;Ahn et al 2017). However, it was also revealed by these studies that the skill of MJO simulation always varied from one model to another, and most models showed limitations in one or more aspects.…”

Subseasonal to Seasonal Prediction of Weather to Climate with Application to Tropical Cyclones

“…As in the case of TC seasonal forecasts, the first subseasonal forecasts of TC activity were statistical (Leroy & Wheeler, ), based on logistic regression of TC days with MJO and Indo‐Pacific SST indices, given that the dynamical models were not able to simulate well or forecast the MJO (Ahn et al, ; Kim et al, ). As the simulations the MJO in dynamical models improved they were able to reproduce the observed modulation of TCs (Vitart, ), as shown in Figure , which led to the development of the TCs subseasonal forecasts with skill similar to the statistical ones (Vitart et al, ).…”