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centurial projections. Correct initialization of the model is known to improve forecast quality on horizons of several years ( Doblas-Reyes et al. 2013 ). Such predictions require Earth system models that include coupled feedbacks between the atmosphere and the bio-, hydro-, and cryospheres across all time scales, and that incorporate greenhouse gases. The largest uncertainty on seasonal-to-decadal time scales is attributed to model error ( Hawkins and Sutton 2009 ). Models are affected by errors due to
centurial projections. Correct initialization of the model is known to improve forecast quality on horizons of several years ( Doblas-Reyes et al. 2013 ). Such predictions require Earth system models that include coupled feedbacks between the atmosphere and the bio-, hydro-, and cryospheres across all time scales, and that incorporate greenhouse gases. The largest uncertainty on seasonal-to-decadal time scales is attributed to model error ( Hawkins and Sutton 2009 ). Models are affected by errors due to
1. Introduction A multimodel ensemble aims to cope with model imperfections in numerical weather prediction (NWP) and has been studied extensively in recent years. In operational NWP, ensemble predictions with perturbed initial conditions have widely been used to evaluate the forecast uncertainties due to the initial condition uncertainties. This type of ensemble prediction system (EPS) was developed based on the theory of error growth due to the chaotic nature of the atmosphere (e.g., Yoden
1. Introduction A multimodel ensemble aims to cope with model imperfections in numerical weather prediction (NWP) and has been studied extensively in recent years. In operational NWP, ensemble predictions with perturbed initial conditions have widely been used to evaluate the forecast uncertainties due to the initial condition uncertainties. This type of ensemble prediction system (EPS) was developed based on the theory of error growth due to the chaotic nature of the atmosphere (e.g., Yoden
performed in the 27- and 9-km domains, while all forecasts are conducted in all three domains. The initial conditions from 3 km are interpolated from the 9-km domain. All domains have 37 vertical levels from the surface to 50 hPa. Results shown in all figures are from the 3-km domain. Physical parameterization options include the WRF single-moment 6-class microphysics scheme (WSM6; Hong and Lim 2006 ), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer scheme ( Mellor and Yamada 1982 ), the Kain
performed in the 27- and 9-km domains, while all forecasts are conducted in all three domains. The initial conditions from 3 km are interpolated from the 9-km domain. All domains have 37 vertical levels from the surface to 50 hPa. Results shown in all figures are from the 3-km domain. Physical parameterization options include the WRF single-moment 6-class microphysics scheme (WSM6; Hong and Lim 2006 ), the Mellor–Yamada–Janjic (MYJ) planetary boundary layer scheme ( Mellor and Yamada 1982 ), the Kain
model and thus, in this sense, this approach is more consistent with assumptions made in data assimilation schemes. Over North America, Meng and Zhang (2007) showed that using a combination of different cumulus parameterization schemes improves the performance of the EnKF: this experiment had a smaller bias and a better background error covariance structure than the single scheme. They also found that including model uncertainties from planetary boundary layer (PBL) and microphysical processes had
model and thus, in this sense, this approach is more consistent with assumptions made in data assimilation schemes. Over North America, Meng and Zhang (2007) showed that using a combination of different cumulus parameterization schemes improves the performance of the EnKF: this experiment had a smaller bias and a better background error covariance structure than the single scheme. They also found that including model uncertainties from planetary boundary layer (PBL) and microphysical processes had
convection scheme ( Bélair et al. 2005 ) by eliminating a dry-adiabatic base in the plume model component of the parameterization. A series of forecasts shows no impact from this correction when evaluated against surface or radiosonde observations, but yields a 2% increase in global cloud cover and a 1% increase in precipitation. An analysis of the planetary albedo suggests that this adjustment is small and potentially beneficial given that comparisons against satellite and surface observations show that
convection scheme ( Bélair et al. 2005 ) by eliminating a dry-adiabatic base in the plume model component of the parameterization. A series of forecasts shows no impact from this correction when evaluated against surface or radiosonde observations, but yields a 2% increase in global cloud cover and a 1% increase in precipitation. An analysis of the planetary albedo suggests that this adjustment is small and potentially beneficial given that comparisons against satellite and surface observations show that
