Effects of Moisture Initialization on MJO and its Teleconnection Prediction in BCC Subseasonal Coupled Model

Abstract

It is well recognized that moisture is of great importance in the representation of the Madden-Julian Oscillation (MJO). In this study, a moisture initialization scheme is developed using the Beijing Climate Center (BCC) subseasonal operational coupled model, and its effects on MJO predictions are investigated. Three sets of hindcast experiments are conducted: a reference experiment without moisture initialization (REF) and two sensitivity experiments with moderate and strong moisture nudging schemes. We show that the prediction skill of the real-time multivariate MJO index is significantly improved in the two sensitivity experiments, where the skillful prediction can reach up to 20 days using the moderate nudging scheme. Larger improvements can be achieved when MJO was initiated from Phases 2-3 and 5-7 or during La Niña episodes, implying that the MJO prediction skill is more sensitive to moisture conditions in active convection regions and backgrounds. The moisture budget analysis indicated that the MJO prediction improvement using the moisture initialization schemes comes from the realistic representation of vertical moisture structure and vertical moisture advection to the east of the MJO convection in the planetary boundary layer. The improved MJO prediction is also demonstrated to enable a more reliable subseasonal prediction of extratropical circulation and precipitation through a more realistic description of MJO-related teleconnections. In addition, the ensemble mean of the three schemes can extend the MJO prediction skill to more than 22 days, showing the potential benefits of ensemble prediction of multiinitialization schemes.

Improved MJO prediction using a multi-member subseasonal to seasonal forecast system of NUIST (NUIST CFS 1.1)

“…Interestingly, these model deficiencies can be partly reduced at the starting stage of the forecast, indicative of the positive influence of the initialization in compensating for the errors in the physical parameterization during the forecast. Given the great importance of moisture processes within the MJO dynamics (e.g., Sobel et al 2001;Adames and Kim 2016), some studies have tested the benefit of moisture initialization in the MJO prediction (e.g., Wu et al 2020). Here we also attempt to include a moisture initialization by substituting the specific humidity in the ICs with the reanalysis values, which remove the initial errors of moisture.…”

“…Recently, more accurate observations have become available (e.g., Vitart et al 2007), and some progresses are made in advancing the data assimilation (e.g., Wu et al 2020), which favors generating high-quality initial conditions of atmosphere and ocean for the MJO prediction. Moreover, ensemble prediction is widely used to reduce the uncertainty and hence improve the skill in weather and climate prediction (e.g., Molteni et al 1996;Slingo and Palmer 2011;Luo et al 2016).…”

A Multivariate Index for Tropical Intraseasonal Oscillations Based on the Seasonally‐Varying Modal Structures

“…Yet another key application of an ISO index is its use in numerical forecasts, as ISO indices help evaluate model performance in predicting the ISOs. In this section, we compute the real‐time MII from output from the European Centre for Medium‐Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP) CFSv2, and China Meteorological Administration (CMA) version 1 (Beijing Climate Center Climate System Model 1.2, BCC‐CSM1.2, Wu et al., 2014; Wu et al., 2020) and version 2 (BCC‐CSM2‐High Resolution, BCC‐CSM2‐HR, Wu et al., 2021), as part of the WMO/S2S reforecast data set (Vitart et al., 2017). The CMAv2 (BCC‐CSM1.2) was a major upgrade of CMAv1 (BCC‐CSM2‐HR) in that model resolution is greatly improved, and the reforecasts switched from the fixed scheme to the “on‐the‐fly” approach for the past 15 years since November 2019.…”

Climatological diagnostics and subseasonal‐to‐seasonal predictions of Madden–Julian Oscillation events

“…The gap between the MJO prediction skill and potential predictability has been narrowed remarkably over the past decade (Kim et al ., 2018), which is attributed to the improvements in the model initialization (Ren et al ., 2016; Wu et al ., 2020), ensemble strategy (Hudson et al ., 2013; Pegion et al ., 2019), air–sea coupling (Fu et al ., 2013; Li et al, 2019), convection parameterization (Zhu et al, 2018; Liu et al ., 2019) and error correction (Kim et al ., 2021; Wu and Jin, 2021). There are several projects to improve the subseasonal‐to‐seasonal prediction skills, such as the World Meteorological Organization Subseasonal‐to‐Seasonal (S2S) prediction project and the Subseasonal Experiment (SubX) prediction project operated by the National Oceanic and Atmospheric Administration (NOAA).…”