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

Liu, Xiangwen; Li, Weijing; Wu, Tongwen; Li, Tim; Gu, W; Bo, Zongkai; Yang, Bowen; Zhang, Li; Wei, Jie

doi:10.1007/s00382-018-4369-y

Cited by 23 publications

(27 citation statements)

References 67 publications

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“…The ocean and sea ice components are the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 4 (MOM4) and Sea Ice Simulator, respectively. The BCC model has been used in short-term climate operational prediction with reasonable skills in forecasting major climate variability at seasonal and sub-seasonal time scales (Liu et al 2015a(Liu et al , 2018.…”

Section: Model and Datamentioning

confidence: 99%

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Liu

et al. 2020

Theor Appl Climatol

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In this paper, we evaluate the capability of the Beijing Climate Center Climate System Model (BCC-CSM) in simulating and forecasting the boreal summer intraseasonal oscillation (BSISO), using its simulation and sub-seasonal to seasonal (S2S) hindcast results. Results show that the model can generally simulate the spatial structure of the BSISO, but give relatively weaker strength, shorter period, and faster transition of BSISO phases when compared with the observations. This partially limits the model’s capability in forecasting the BSISO, with a useful skill of only 9 days. Two sets of hindcast experiments with improved atmospheric and atmosphere/ocean initial conditions (referred to as EXP1 and EXP2, respectively) are conducted to improve the BSISO forecast. The BSISO forecast skill is increased by 2 days with the optimization of atmospheric initial conditions only (EXP1), and is further increased by 1 day with the optimization of both atmospheric and oceanic initial conditions (EXP2). These changes lead to a final skill of 12 days, which is comparable to the skills of most models participated in the S2S Prediction Project. In EXP1 and EXP2, the BSISO forecast skills are improved for most initial phases, especially phases 1 and 2, denoting a better description for BSISO propagation from the tropical Indian Ocean to the western North Pacific. However, the skill is considerably low and insensitive to initial conditions for initial phase 6 and target phase 3, corresponding to the BSISO convection’s active-to-break transition over the western North Pacific and BSISO convection’s break-to-active transition over the tropical Indian Ocean and Maritime Continent. This prediction barrier also exists in many forecast models of the S2S Prediction Project. Our hindcast experiments with different initial conditions indicate that the remarkable model errors over the Maritime Continent and subtropical western North Pacific may largely account for the prediction barrier.

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Section: Model and Datamentioning

confidence: 99%

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Liu

et al. 2020

Theor Appl Climatol

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“…Using this database, the prediction skill of numerical models in operational centers can be evaluated and compared, and thus the common and different features among these models can be further investigated. For example, recently, the forecast skill of winter MJO (Vitart 2017;Liu et al 2017Liu et al , 2018 and intra-seasonal variation of Asian summer monsoon (Jie et al 2017) have been assessed through using the S2S project database. Furthermore, the S2S project presents an opportunity for the understanding of the relationship between MJO and model prediction skill in extratropics.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database

et al. 2018

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The reforecast of 11 models in the sub-seasonal to seasonal (S2S) prediction project has been analyzed to investigate the effects of the Madden-Julian Oscillation (MJO) on the prediction skill of winter 2-m air temperature (T2M) over China. Most of the S2S models have useful prediction skills (correlation skill ≥ 0.5) before pentads 3 and 4. ECMWF model can possess a good prediction skill for almost four pentads and perform the best among the 11 models. ECCC and ECMWF models have more reliable ensemble prediction and better ensemble strategies than the other models. All the models tend to have lower T2M prediction skill over the Tibetan Plateau than that over the other regions of China. Moreover, initial state and model resolution have important influences on S2S prediction skill. In most of the models at pentads 3 and 4, T2M prediction skill of forecast with MJO at initial time is significantly higher than that without over parts of China. However, the spatial distributions of the prediction skill differences due to MJO are not consistent among the 11 models. This indicates that there is an uncertainty of the effects of MJO on T2M prediction over China at pentads 3 and 4. Planetary-scale teleconnection pattern excited by MJO over the Northern Hemisphere is the possible reason for the effect of MJO on T2M prediction skill. Because most of the models can maintain this teleconnection pattern for 3-4 forecast pentads, MJO can affect the atmospheric circulation over China during this period, and improve the T2M prediction skill in the models. This finding suggests that the prediction of winter T2M over China initialized with MJO can be more skillful at pentads 3 and 4 than that without MJO in the initial conditions.

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“…We consider that the low skill of the MJO simulation in CTL is largely because convection often occurs too easily in the original ZM scheme. Previous studies have shown that tuning several key parameters in the convective scheme can considerably improve the MJO simulation (Boyle et al., 2015; Liu et al., 2019). Our preliminary test experiments also indicate that the MJO simulation in REV can be further improved when the overall intensity of convection is suppressed via using a different value for

η_{0}

a

in Equation .…”

Section: Resultsmentioning

confidence: 99%

“…Meanwhile, several studies have shown that the simulated precipitation diurnal cycle can be improved via using different functions for convection triggering (Tawfik et al., 2017; Xie et al., 2019; S. Xie & Zhang, 2000), employing new diagnostic or prognostic quantities for convective closure (Fuchs & Raymond, 2007), or modifying the formulation of convective entrainment rates (Piriou et al., 2007; Stirling & Stratton, 2012). It was also reported that structural modifications or parameter optimizations in convective parameterizations can improve the simulations of MJO and ENSO (Boyle et al., 2015; Liu et al., 2019; Lu & Ren, 2016; Neale et al., 2008) in GCMs.…”

Section: Introductionmentioning

confidence: 99%

Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability

Yang

Wang

Zhang

et al. 2021

J Adv Model Earth Syst

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We modify the Zhang-McFarlane deep convection scheme in the Community Atmosphere Model version 5 to couple it with a unified parameterization for boundary-layer turbulence and shallow convection, i.e. Cloud Layers Unified by Binormals (CLUBB). By assuming a lognormal distribution of entrainment rate across the entire moist convective regimes, we link mass fluxes between shallow and deep convection, which are partitioned by the entrainment rate of the shallowest deep convective plume. Hence, a new deep convective closure is established which is coupled to the sub-grid vertical motion variability in CLUBB. The convection feedback (or memory) effects are also considered to decrease the entrainment spectrum width and enhance the vertical velocity variability that further affect deep convection. Results show that the revised scheme improves the precipitation simulations in terms of the mean state and variability at various timescales, such as the alleviated double-intertropical convergence zone and more realistic simulations of the seasonal variation of monsoon precipitation over East Asia, Madden-Julian Oscillation, and precipitation diurnal phase propagations downstream of large terrains. The improvements are still seen in many aspects such as the mean-state precipitation when turning off the convection feedback impacts in the revised scheme, emphasizing the benefits of using the modified mass-flux closure. However, the convection feedbacks have considerable effects on the precipitation diurnal cycle simulations over regions with late-afternoon precipitation peaks. Overall, the revised scheme provides a unified treatment for sub-grid vertical motions across regimes of boundary-layer turbulence, shallow convection and deep convection, leading to better-simulated precipitation at various timescales.

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

Cited by 23 publications

References 67 publications

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Impacts of atmospheric and oceanic initial conditions on boreal summer intraseasonal oscillation forecast in the BCC model

Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database

Linking Deep and Shallow Convective Mass Fluxes via an Assumed Entrainment Distribution in CAM5‐CLUBB: Parameterization and Simulated Precipitation Variability

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