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the ocean grid points, in the Northern Hemisphere tropics (0°–30°N) for the months of August to October (ASO) and in the Southern Hemisphere tropics (30°S–0) for the months of January to March (JFM). As in the case of NTC and ACE, we exclude the South Atlantic and the southeast Pacific (east of 250°E) in our analysis. Similarly, the biases in the models’ environmental field climatologies relative to the ERA-Interim climatology are quantified using two measures: spatial correlation and root
the ocean grid points, in the Northern Hemisphere tropics (0°–30°N) for the months of August to October (ASO) and in the Southern Hemisphere tropics (30°S–0) for the months of January to March (JFM). As in the case of NTC and ACE, we exclude the South Atlantic and the southeast Pacific (east of 250°E) in our analysis. Similarly, the biases in the models’ environmental field climatologies relative to the ERA-Interim climatology are quantified using two measures: spatial correlation and root
MCS initiated within the red box region in the inset map. Figure 7 shows the spatial distribution of the model biases in total precipitation, MCS precipitation and MCS number in spring (March–May). Both simulations generally underestimate the total precipitation over the central United States, but overestimate it over the Rocky Mountains and eastern United States. The dry bias for MCS precipitation is much larger, particularly over the Southern Great Plains (SGP) region, where MCS precipitation
MCS initiated within the red box region in the inset map. Figure 7 shows the spatial distribution of the model biases in total precipitation, MCS precipitation and MCS number in spring (March–May). Both simulations generally underestimate the total precipitation over the central United States, but overestimate it over the Rocky Mountains and eastern United States. The dry bias for MCS precipitation is much larger, particularly over the Southern Great Plains (SGP) region, where MCS precipitation
1. Introduction Over the past decade, cloud cover biases in the Southern Hemisphere oceans have been identified ( Trenberth and Fasullo 2010 ) and investigated in a large number of general circulation models (GCMs) and reanalysis products. Bodas-Salcedo et al. (2012 , 2014 ) demonstrated that biases in shortwave absorption at the surface (an issue dominating during the austral summer) stem from deficiencies in the low and midlevel clouds typically found in the cold sector of extratropical
1. Introduction Over the past decade, cloud cover biases in the Southern Hemisphere oceans have been identified ( Trenberth and Fasullo 2010 ) and investigated in a large number of general circulation models (GCMs) and reanalysis products. Bodas-Salcedo et al. (2012 , 2014 ) demonstrated that biases in shortwave absorption at the surface (an issue dominating during the austral summer) stem from deficiencies in the low and midlevel clouds typically found in the cold sector of extratropical
Joyce 2013 ; O’Reilly and Czaja 2015 ). In the Southern Ocean, south of the Indian Ocean, the Agulhas Return Current (ARC) helps to anchor the climatological location of the free-tropospheric storm track ( Nakamura et al. 2004 ). This causes the region to have a consistent storm track throughout the year, which, for the Southern Ocean storm track, is a trait that is unique to the ARC region. These examples of the oceans influencing the storm tracks primarily focus on the free-tropospheric storm
Joyce 2013 ; O’Reilly and Czaja 2015 ). In the Southern Ocean, south of the Indian Ocean, the Agulhas Return Current (ARC) helps to anchor the climatological location of the free-tropospheric storm track ( Nakamura et al. 2004 ). This causes the region to have a consistent storm track throughout the year, which, for the Southern Ocean storm track, is a trait that is unique to the ARC region. These examples of the oceans influencing the storm tracks primarily focus on the free-tropospheric storm
), suggesting that its cause lies outside the Arabian Sea. (Determining the cause of this deep bias is beyond the scope of this paper. It is possibly linked to errors in the Southern Hemisphere processes that determine thermocline and intermediate waters.) In the upper ocean, the negative density bias south of 10°N results from excessive rainfall in the western equatorial Indian Ocean ( Figs. 10e–h below). North of about 10°N, becomes positive in a wedge-shaped region in the upper ocean where near
), suggesting that its cause lies outside the Arabian Sea. (Determining the cause of this deep bias is beyond the scope of this paper. It is possibly linked to errors in the Southern Hemisphere processes that determine thermocline and intermediate waters.) In the upper ocean, the negative density bias south of 10°N results from excessive rainfall in the western equatorial Indian Ocean ( Figs. 10e–h below). North of about 10°N, becomes positive in a wedge-shaped region in the upper ocean where near
socioeconomic value, and physics-oriented model evaluation is an indispensable part of the effort. Skillful seasonal prediction is related to several sources of predictability, including inertia, external forcing, and patterns of variability ( National Research Council 2010 ). Recurrent modes of low-frequency variability, which arise from the interaction between different components of the climate system, such as El Niño–Southern Oscillation (ENSO), the Madden–Julian oscillation (MJO), and the annular modes
socioeconomic value, and physics-oriented model evaluation is an indispensable part of the effort. Skillful seasonal prediction is related to several sources of predictability, including inertia, external forcing, and patterns of variability ( National Research Council 2010 ). Recurrent modes of low-frequency variability, which arise from the interaction between different components of the climate system, such as El Niño–Southern Oscillation (ENSO), the Madden–Julian oscillation (MJO), and the annular modes
hemisphere, considering seasonal values over June–August in the Northern Hemisphere and December–February in the Southern Hemisphere. To characterize SM–ET coupling, we analyze output of surface (top 10 cm; variable mrsos in the CMIP5 archive) soil moisture and evapotranspiration. We use surface soil moisture because it is more easily comparable across models, when correlated with surface fluxes, than total (column integrated) soil moisture, which reflects differences in soil depths between models. With
hemisphere, considering seasonal values over June–August in the Northern Hemisphere and December–February in the Southern Hemisphere. To characterize SM–ET coupling, we analyze output of surface (top 10 cm; variable mrsos in the CMIP5 archive) soil moisture and evapotranspiration. We use surface soil moisture because it is more easily comparable across models, when correlated with surface fluxes, than total (column integrated) soil moisture, which reflects differences in soil depths between models. With
-017-0008-2 Nakajima , T. Y. , K. Suzuki , and G. L. Stephens , 2010 : Droplet growth in warm water clouds observed by the A-Train. Part II: A multisensor view . J. Atmos. Sci. , 67 , 1897 – 1907 , https://doi.org/10.1175/2010JAS3276.1 . 10.1175/2010JAS3276.1 Naud , C. M. , J. F. Booth , and A. D. Del Genio , 2014 : Evaluation of ERA-Interim and MERRA cloudiness in the Southern Ocean . J. Climate , 27 , 2109 – 2124 , https://doi.org/10.1175/JCLI-D-13-00432.1 . 10.1175/JCLI-D-13
-017-0008-2 Nakajima , T. Y. , K. Suzuki , and G. L. Stephens , 2010 : Droplet growth in warm water clouds observed by the A-Train. Part II: A multisensor view . J. Atmos. Sci. , 67 , 1897 – 1907 , https://doi.org/10.1175/2010JAS3276.1 . 10.1175/2010JAS3276.1 Naud , C. M. , J. F. Booth , and A. D. Del Genio , 2014 : Evaluation of ERA-Interim and MERRA cloudiness in the Southern Ocean . J. Climate , 27 , 2109 – 2124 , https://doi.org/10.1175/JCLI-D-13-00432.1 . 10.1175/JCLI-D-13
above-critical events, which are responsible for most of the precipitation over tropical oceans (except in dry regions). It also captures the seasonal shifts of convergence zones, for example, the local maximum along 10°S in the Indian Ocean and between 0° and 10°S in the eastern Pacific results from events during the Southern Hemisphere raining seasons. Note that Fig. 5e [and the conditional probability ; Fig. S14d ] has a geographic pattern similar to Fig. 17 in Tao and Moncrieff (2009
above-critical events, which are responsible for most of the precipitation over tropical oceans (except in dry regions). It also captures the seasonal shifts of convergence zones, for example, the local maximum along 10°S in the Indian Ocean and between 0° and 10°S in the eastern Pacific results from events during the Southern Hemisphere raining seasons. Note that Fig. 5e [and the conditional probability ; Fig. S14d ] has a geographic pattern similar to Fig. 17 in Tao and Moncrieff (2009
1. Introduction The Madden–Julian oscillation (MJO) ( Madden and Julian 1971 , 1972 ) is the dominant mode of tropical intraseasonal variability. It is characterized by a convection–circulation coupled system propagating eastward from the Indian Ocean to the Pacific with periods ranging from approximately 30 to 60 days. The MJO modulates atmospheric (e.g., tropical cyclones), oceanic (e.g., chlorophyll), and ocean–atmosphere coupled [e.g., El Niño–Southern Oscillation (ENSO)] disturbances
1. Introduction The Madden–Julian oscillation (MJO) ( Madden and Julian 1971 , 1972 ) is the dominant mode of tropical intraseasonal variability. It is characterized by a convection–circulation coupled system propagating eastward from the Indian Ocean to the Pacific with periods ranging from approximately 30 to 60 days. The MJO modulates atmospheric (e.g., tropical cyclones), oceanic (e.g., chlorophyll), and ocean–atmosphere coupled [e.g., El Niño–Southern Oscillation (ENSO)] disturbances
