Publication | Open Access
Improving synoptic and intraseasonal variability in CFSv2 via stochastic representation of organized convection
52
Citations
45
References
2017
Year
EngineeringFluid MechanicsWeather ForecastingClimate ModelingEarth System ScienceEarth ScienceStochastic Multicloud ModelNumerical Weather PredictionAtmospheric ScienceClimate ProjectionAtmospheric ModelingIntraseasonal VariabilityBiophysicsClimate ForecastingClimate ChangeClimate SciencesMeteorologyGeographyStochastic RepresentationForecastingMadden‐julian OscillationClimate DynamicsClimatologyEnvironmental Fluid DynamicHydrodynamicsCfs‐smcm SimulationClimate ModellingMultiscale Modeling
Abstract To better represent organized convection in the Climate Forecast System version 2 (CFSv2), a stochastic multicloud model (SMCM) parameterization is adopted and a 15 year climate run is made. The last 10 years of simulations are analyzed here. While retaining an equally good mean state (if not better) as the parent model, the CFS‐SMCM simulation shows significant improvement in the synoptic and intraseasonal variability. The CFS‐SMCM provides a better account of convectively coupled equatorial waves and the Madden‐Julian oscillation. The CFS‐SMCM exhibits improvements in northward and eastward propagation of intraseasonal oscillation of convection including the MJO propagation beyond the maritime continent barrier, which is the Achilles Heel for coarse‐resolution global climate models (GCMs). The distribution of precipitation events is better simulated in CFSsmcm and spreads naturally toward high‐precipitation events. Deterministic GCMs tend to simulate a narrow distribution with too much drizzling precipitation and too little high‐precipitation events.
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