Search Results
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
-term forecasting purposes, since models adopted for weather forecasting or reanalysis share common components with climate models. Many conventional diagnostics for climate models emphasize comparisons against long-term climatology or variability at different time scales, and the model performance examined by these metrics is affected by multiple factors. While sensitivity experiments with respect to such metrics are useful in identifying important processes ( Benedict et al. 2013 , 2014 ; Boyle et al. 2015
-term forecasting purposes, since models adopted for weather forecasting or reanalysis share common components with climate models. Many conventional diagnostics for climate models emphasize comparisons against long-term climatology or variability at different time scales, and the model performance examined by these metrics is affected by multiple factors. While sensitivity experiments with respect to such metrics are useful in identifying important processes ( Benedict et al. 2013 , 2014 ; Boyle et al. 2015
convective systems contributing more to the rapid precipitation increases. The proposed physical argument for the precipitation onset is buoyancy centric. Holloway and Neelin (2009 , hereafter HN09 ) showed, using a steady-state entraining plume model, that for environmental moisture values at and beyond precipitation onset, the entraining plumes are positively buoyant near the freezing level. The implication is that if a convective entity—often represented by a bulk-entraining plume—can survive mixing
convective systems contributing more to the rapid precipitation increases. The proposed physical argument for the precipitation onset is buoyancy centric. Holloway and Neelin (2009 , hereafter HN09 ) showed, using a steady-state entraining plume model, that for environmental moisture values at and beyond precipitation onset, the entraining plumes are positively buoyant near the freezing level. The implication is that if a convective entity—often represented by a bulk-entraining plume—can survive mixing
