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. 2013 ; Ahn et al. 2017 ). Performance-based MJO simulation diagnostics and metrics have been developed for a consistent evaluation of the MJO fidelity in models ( Waliser et al. 2009 ). Although MJO simulation has generally improved in models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) over CMIP3 models in terms of MJO variance and eastward propagation ( Hung et al. 2013 ), models still tend to produce a weaker MJO with faster eastward propagation (e.g., Kim et al. 2014a
. 2013 ; Ahn et al. 2017 ). Performance-based MJO simulation diagnostics and metrics have been developed for a consistent evaluation of the MJO fidelity in models ( Waliser et al. 2009 ). Although MJO simulation has generally improved in models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) over CMIP3 models in terms of MJO variance and eastward propagation ( Hung et al. 2013 ), models still tend to produce a weaker MJO with faster eastward propagation (e.g., Kim et al. 2014a
different resolutions are needed. In this study, we develop a process-oriented approach to systematically evaluate the performance of climate models at mesoscale resolutions (grid spacing 10–50 km) in simulating warm-season MCS-like precipitation features and their favorable large-scale environments over the United States. Climate simulations at mesoscale resolutions are becoming more commonly available (e.g., Caldwell et al. 2019 ; Gutjahr et al. 2019 ; Roberts et al. 2019 ), motivating the need to
different resolutions are needed. In this study, we develop a process-oriented approach to systematically evaluate the performance of climate models at mesoscale resolutions (grid spacing 10–50 km) in simulating warm-season MCS-like precipitation features and their favorable large-scale environments over the United States. Climate simulations at mesoscale resolutions are becoming more commonly available (e.g., Caldwell et al. 2019 ; Gutjahr et al. 2019 ; Roberts et al. 2019 ), motivating the need to
. 2018b ), in part because of the nonlinear nature of the model physics. Because these two issues cannot easily be untangled, the goal of this study is therefore to examine both the impact of the convection parameterization and of the tuning of cloud parameters on the model representation of cloud cover in the cold sector of extratropical cyclones. To do this, we take advantage of metrics designed to evaluate modeled cloud cover in the cold sector of extratropical cyclones (e.g., Naud et al. 2014
. 2018b ), in part because of the nonlinear nature of the model physics. Because these two issues cannot easily be untangled, the goal of this study is therefore to examine both the impact of the convection parameterization and of the tuning of cloud parameters on the model representation of cloud cover in the cold sector of extratropical cyclones. To do this, we take advantage of metrics designed to evaluate modeled cloud cover in the cold sector of extratropical cyclones (e.g., Naud et al. 2014
differences in their simulation of land–atmosphere fluxes, including ET (e.g., Mueller and Seneviratne 2014 , and references therein). Much research has been directed over the past decade toward evaluating the representation of ET in climate models, based on global land ET products derived from observations, such as remote sensing data, upscaled in situ measurements, and/or land surface models driven by observations (e.g., Mueller et al. 2013 ). Perhaps less attention has been devoted, until recently
differences in their simulation of land–atmosphere fluxes, including ET (e.g., Mueller and Seneviratne 2014 , and references therein). Much research has been directed over the past decade toward evaluating the representation of ET in climate models, based on global land ET products derived from observations, such as remote sensing data, upscaled in situ measurements, and/or land surface models driven by observations (e.g., Mueller et al. 2013 ). Perhaps less attention has been devoted, until recently
.1175/JCLI4282.1 Camargo , S. J. , A. H. Sobel , A. G. Barnston , and K. A. Emanuel , 2007b : Tropical cyclone genesis potential index in climate models . Tellus , 59A , 428 – 443 , https://doi.org/10.1111/j.1600-0870.2007.00238.x . 10.1111/j.1600-0870.2007.00238.x Camargo , S. J. , M. K. Tippett , A. H. Sobel , G. A. Vecchi , and M. Zhao , 2014 : Testing the performance of tropical cyclone genesis indices in future climates using the HIRAM model . J. Climate , 27 , 9171
.1175/JCLI4282.1 Camargo , S. J. , A. H. Sobel , A. G. Barnston , and K. A. Emanuel , 2007b : Tropical cyclone genesis potential index in climate models . Tellus , 59A , 428 – 443 , https://doi.org/10.1111/j.1600-0870.2007.00238.x . 10.1111/j.1600-0870.2007.00238.x Camargo , S. J. , M. K. Tippett , A. H. Sobel , G. A. Vecchi , and M. Zhao , 2014 : Testing the performance of tropical cyclone genesis indices in future climates using the HIRAM model . J. Climate , 27 , 9171
. Since for in (2b) , is the depth-integrated pressure anomaly relative to its value off Sumatra. In Fig. 6b , we used ERA-Interim winds to evaluate (2). Sverdrup (1947) derived solution (2) from depth-integrated equations, and in so doing lost all information about the vertical structure of the flow. In a model that allows for vertical structure, baroclinic adjustments (namely, the radiation of baroclinic Rossby waves across the basin) tend to trap the Sverdrup flow in the upper ocean (e
. Since for in (2b) , is the depth-integrated pressure anomaly relative to its value off Sumatra. In Fig. 6b , we used ERA-Interim winds to evaluate (2). Sverdrup (1947) derived solution (2) from depth-integrated equations, and in so doing lost all information about the vertical structure of the flow. In a model that allows for vertical structure, baroclinic adjustments (namely, the radiation of baroclinic Rossby waves across the basin) tend to trap the Sverdrup flow in the upper ocean (e
model stands out for having a convective fraction of 40%, even in cyclones with heavy precipitation. The unique behavior of the GISS model is also evident in the composite mean plot for the precipitation from the convection scheme, as it has the strongest rates in the warm sector near the cyclone center. Despite these differences, the overall performance of the GISS model in generating ETC precipitation matched ERAI and the GFDL model. The nonnegligible contribution of the convection scheme to total
model stands out for having a convective fraction of 40%, even in cyclones with heavy precipitation. The unique behavior of the GISS model is also evident in the composite mean plot for the precipitation from the convection scheme, as it has the strongest rates in the warm sector near the cyclone center. Despite these differences, the overall performance of the GISS model in generating ETC precipitation matched ERAI and the GFDL model. The nonnegligible contribution of the convection scheme to total
focus on the role of the model MJO and basic state quality on MJO teleconnection patterns. The CMIP5 models and reference datasets are described in section 2 , as well as a description of the general methodology of the study. Section 3 investigates the ability of the CMIP5 models to correctly simulate the MJO, basic state, and the MJO teleconnection patterns. In section 4 , a linear baroclinic model (LBM) is employed to analyze the individual impacts of the model MJO and basic state performance
focus on the role of the model MJO and basic state quality on MJO teleconnection patterns. The CMIP5 models and reference datasets are described in section 2 , as well as a description of the general methodology of the study. Section 3 investigates the ability of the CMIP5 models to correctly simulate the MJO, basic state, and the MJO teleconnection patterns. In section 4 , a linear baroclinic model (LBM) is employed to analyze the individual impacts of the model MJO and basic state performance
. , and Coauthors , 2013 : The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation . Geosci. Model Dev. , 6 , 687 – 720 , doi: 10.5194/gmdd-5-2843-2012 . 10.5194/gmd-6-687-2013 Blackmon , M. L. , 1976 : A climatological spectral study of the 500 mb geopotential height of the Northern Hemisphere . J. Atmos. Sci. , 33 , 1607 – 1623 , doi: 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2 . 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2 Booth , J. F. , L
. , and Coauthors , 2013 : The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation . Geosci. Model Dev. , 6 , 687 – 720 , doi: 10.5194/gmdd-5-2843-2012 . 10.5194/gmd-6-687-2013 Blackmon , M. L. , 1976 : A climatological spectral study of the 500 mb geopotential height of the Northern Hemisphere . J. Atmos. Sci. , 33 , 1607 – 1623 , doi: 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2 . 10.1175/1520-0469(1976)033<1607:ACSSOT>2.0.CO;2 Booth , J. F. , L
represent ENSO. An NAO index is calculated as the difference in the standardized SLP between Reykjavik, Iceland, and Lisbon, Portugal ( Jones et al. 1997 ). A cutoff of ±0.5 standard deviation is used to select the ENSO and NAO years in composite analysis. b. Performance metrics The anomaly correlation coefficient (ACC) is calculated to evaluate the model prediction skill. The ACC is calculated by The metric evaluates the anomalies of the forecast and observations, which are defined with respect to the
represent ENSO. An NAO index is calculated as the difference in the standardized SLP between Reykjavik, Iceland, and Lisbon, Portugal ( Jones et al. 1997 ). A cutoff of ±0.5 standard deviation is used to select the ENSO and NAO years in composite analysis. b. Performance metrics The anomaly correlation coefficient (ACC) is calculated to evaluate the model prediction skill. The ACC is calculated by The metric evaluates the anomalies of the forecast and observations, which are defined with respect to the
