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noise drawn from the 3DVAR static covariance was used to parameterize model errors. In practice, such random perturbations are easy to obtain. To test the background ensemble perturbation in Eq. (4) , Fig. 1b shows the root-mean-square (rms) background error and the ensemble spread as a function of latitude for an experiment with the hybrid scheme ( α = 0.4 and the localization scale = 15 000 km). As a comparison, another experiment where no additive noise was applied and thus a global constant
noise drawn from the 3DVAR static covariance was used to parameterize model errors. In practice, such random perturbations are easy to obtain. To test the background ensemble perturbation in Eq. (4) , Fig. 1b shows the root-mean-square (rms) background error and the ensemble spread as a function of latitude for an experiment with the hybrid scheme ( α = 0.4 and the localization scale = 15 000 km). As a comparison, another experiment where no additive noise was applied and thus a global constant
framework of the compressible and anelastic models. It also gives the quantitative structure of the diabatic warming, the models’ equations, and the numerical techniques used. Section 3 provides a comparison of the compressible and anelastic model responses to the instantaneous diabatic warming. The study examines the model responses at the initial conditions and at 1 and 10 min. Moreover, the structure of the Lamb wave packet excited in the compressible model is examined. This section also briefly
framework of the compressible and anelastic models. It also gives the quantitative structure of the diabatic warming, the models’ equations, and the numerical techniques used. Section 3 provides a comparison of the compressible and anelastic model responses to the instantaneous diabatic warming. The study examines the model responses at the initial conditions and at 1 and 10 min. Moreover, the structure of the Lamb wave packet excited in the compressible model is examined. This section also briefly
time ( Taylor et al. 2012 ). Representative concentration pathways (RCPs) have replaced the Special Report on Emission Scenarios (SRESs) as the latest generation of scenarios for long-term experiments. The comparison between the two generations of climate models is important not only for researchers from various fields who will use the model outputs but also for modelers by facilitating further model development. An objective of the present paper is to identify whether there are improvements in the
time ( Taylor et al. 2012 ). Representative concentration pathways (RCPs) have replaced the Special Report on Emission Scenarios (SRESs) as the latest generation of scenarios for long-term experiments. The comparison between the two generations of climate models is important not only for researchers from various fields who will use the model outputs but also for modelers by facilitating further model development. An objective of the present paper is to identify whether there are improvements in the
of urban features are incorporated; the models have varying levels of complexity, and different fluxes modeled ( Table 1 ; Figs. 1 , 2 ). In this paper, the methodology and initial results from the first international comparison of a broad range of urban land-surface schemes are presented. The requirements of a land-surface model from the perspective of an atmospheric model are considered; that is, surface fluxes of heat, moisture, and momentum. Thus, the fundamental requirement for the models
of urban features are incorporated; the models have varying levels of complexity, and different fluxes modeled ( Table 1 ; Figs. 1 , 2 ). In this paper, the methodology and initial results from the first international comparison of a broad range of urban land-surface schemes are presented. The requirements of a land-surface model from the perspective of an atmospheric model are considered; that is, surface fluxes of heat, moisture, and momentum. Thus, the fundamental requirement for the models
the model simulations: total cloud amount (CA), liquid water path (LWP), ice water path (IWP), and top of the atmosphere (ToA) cloud forcing (CF). Both LWP and IWP contribute to CA and CF. Here we focus on CA, LWP, and CF, which we will partition between shortwave cloud forcing (SCF) and longwave cloud forcing (LCF). All three of these cloud parameters are also available from the CMIP3 models, allowing for a side-by-side comparison of CMIP5 with the previous-generation models. This enables us to
the model simulations: total cloud amount (CA), liquid water path (LWP), ice water path (IWP), and top of the atmosphere (ToA) cloud forcing (CF). Both LWP and IWP contribute to CA and CF. Here we focus on CA, LWP, and CF, which we will partition between shortwave cloud forcing (SCF) and longwave cloud forcing (LCF). All three of these cloud parameters are also available from the CMIP3 models, allowing for a side-by-side comparison of CMIP5 with the previous-generation models. This enables us to
America. The squared canonical correlation ρ 2 between NINO34 and the leading PCs of the DJF T2m over North American, as a function of the number of PCs used to represent the data, is shown in the left panels of Fig. 2 for observations and two model simulations. Comparison with the 5% significance curve for ρ 2 , shown as the dashed line in the left panels of Fig. 2 , indicates that the canonical correlation estimated from observations is marginally significant to insignificant for the first few
America. The squared canonical correlation ρ 2 between NINO34 and the leading PCs of the DJF T2m over North American, as a function of the number of PCs used to represent the data, is shown in the left panels of Fig. 2 for observations and two model simulations. Comparison with the 5% significance curve for ρ 2 , shown as the dashed line in the left panels of Fig. 2 , indicates that the canonical correlation estimated from observations is marginally significant to insignificant for the first few
to the environment and not necessarily differences in the storm environments themselves (though such differences could have led to the differences in convective feedbacks). This comparison of the SRH PMMs and KDEs illustrates the importance of spatial field analysis for informed interpretation of low-order model climatologies of storms. The bivariate KDEs reveal intermodel differences in intervariable relationships that the PMMs and univariate KDEs cannot provide. For example, relative to the
to the environment and not necessarily differences in the storm environments themselves (though such differences could have led to the differences in convective feedbacks). This comparison of the SRH PMMs and KDEs illustrates the importance of spatial field analysis for informed interpretation of low-order model climatologies of storms. The bivariate KDEs reveal intermodel differences in intervariable relationships that the PMMs and univariate KDEs cannot provide. For example, relative to the
simulations. The CPs are defined by clusters represented by the pdfs in the GMMs. a. Distance measures based on probabilistic models Apart from a visual comparison of mean values (centroid patterns), a popular quantitative difference measure is the Euclidean distance in the multidimensional space where μ P and μ Q denote the mean values of CPs P and Q . This measure can be used to quantify a difference, however, it includes only first-moment information, disregarding the higher moments describing
simulations. The CPs are defined by clusters represented by the pdfs in the GMMs. a. Distance measures based on probabilistic models Apart from a visual comparison of mean values (centroid patterns), a popular quantitative difference measure is the Euclidean distance in the multidimensional space where μ P and μ Q denote the mean values of CPs P and Q . This measure can be used to quantify a difference, however, it includes only first-moment information, disregarding the higher moments describing
from minor (adding new postprocessing procedures) to major (upgrading the time-stepping scheme). Comparisons with wind tunnel measurements demonstrated that OU-LES is capable of reproducing sheared CBL flows ( Fedorovich et al. 2001 ). Similarly, comparisons with bulk models and water tank data confirmed that OU-LES is able to reproduce turbulent flow regimes associated with free convection in the atmospheric boundary layer ( Fedorovich et al. 2004a ). In addition, OU-LES was compared with five
from minor (adding new postprocessing procedures) to major (upgrading the time-stepping scheme). Comparisons with wind tunnel measurements demonstrated that OU-LES is capable of reproducing sheared CBL flows ( Fedorovich et al. 2001 ). Similarly, comparisons with bulk models and water tank data confirmed that OU-LES is able to reproduce turbulent flow regimes associated with free convection in the atmospheric boundary layer ( Fedorovich et al. 2004a ). In addition, OU-LES was compared with five
; moreover, comparisons with observations are difficult, because u * is a function of scale (e.g., Kustas et al. 2005 ; Strassberg et al. 2008 ). Uncertainty in both modeled and measured subsidence makes it difficult to evaluate its role. Previous evaluations show that PBL schemes that parameterize vertical turbulent fluxes as proportional to the corresponding local vertical gradient tend to underestimate CBL depth; while those allowing for “nonlocal” transport, which account for fluxes by eddies
; moreover, comparisons with observations are difficult, because u * is a function of scale (e.g., Kustas et al. 2005 ; Strassberg et al. 2008 ). Uncertainty in both modeled and measured subsidence makes it difficult to evaluate its role. Previous evaluations show that PBL schemes that parameterize vertical turbulent fluxes as proportional to the corresponding local vertical gradient tend to underestimate CBL depth; while those allowing for “nonlocal” transport, which account for fluxes by eddies
