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1. Introduction In a recent study Shaw et al. (2010) investigated the nature of downward wave coupling between the stratosphere and troposphere using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset. Downward wave coupling occurs when planetary waves reflected in the stratosphere impact the troposphere and is distinct from zonal-mean coupling, which results from wave dissipation and its subsequent impact on the zonal-mean flow ( Perlwitz and
1. Introduction In a recent study Shaw et al. (2010) investigated the nature of downward wave coupling between the stratosphere and troposphere using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset. Downward wave coupling occurs when planetary waves reflected in the stratosphere impact the troposphere and is distinct from zonal-mean coupling, which results from wave dissipation and its subsequent impact on the zonal-mean flow ( Perlwitz and
the possibility that AEW tracks could cluster around different families of trajectories. Among these, Carlson (1969) was perhaps the first to locate, based upon 33 observed waves, a hint of two activity maxima, coincident with two vortices, one at higher elevation (10 000 ft) located at about 12°N and one at lower elevation (2000 ft) at about 20°N, with the former displaying a westward propagation tendency toward the Atlantic Ocean and the latter appearing to vanish along the coastline. Carlson
the possibility that AEW tracks could cluster around different families of trajectories. Among these, Carlson (1969) was perhaps the first to locate, based upon 33 observed waves, a hint of two activity maxima, coincident with two vortices, one at higher elevation (10 000 ft) located at about 12°N and one at lower elevation (2000 ft) at about 20°N, with the former displaying a westward propagation tendency toward the Atlantic Ocean and the latter appearing to vanish along the coastline. Carlson
sources (e.g., Fig. 11 ). It is also worth noting that there is some suggestion of a phase locking of the wave packets defined by the REOFs with the summertime stationary waves. In particular, REOFs 1 and 5 appear to develop in the North Atlantic trough and are distributed to the north and south of the Asian monsoonal high, respectively. REOF 2 coincides with the North Pacific oceanic trough, while REOFs 3 and 4 appear to emerge out of the North Pacific oceanic trough and extend across the North
sources (e.g., Fig. 11 ). It is also worth noting that there is some suggestion of a phase locking of the wave packets defined by the REOFs with the summertime stationary waves. In particular, REOFs 1 and 5 appear to develop in the North Atlantic trough and are distributed to the north and south of the Asian monsoonal high, respectively. REOF 2 coincides with the North Pacific oceanic trough, while REOFs 3 and 4 appear to emerge out of the North Pacific oceanic trough and extend across the North
near-zero K ( y ). In all reanalyses the vorticity structure appears more vertically aligned than in Fig. 3 , and the cyclonic vorticity maximum to the south of the jet level is also substantially stronger than at 0°. The implication is that the formation of a vertically aligned, barotropically unstable column seems to become even more likely as one moves from land (0°) toward the ocean (20°W). This is interesting because some developing waves tend to show vertically aligned structures at these
near-zero K ( y ). In all reanalyses the vorticity structure appears more vertically aligned than in Fig. 3 , and the cyclonic vorticity maximum to the south of the jet level is also substantially stronger than at 0°. The implication is that the formation of a vertically aligned, barotropically unstable column seems to become even more likely as one moves from land (0°) toward the ocean (20°W). This is interesting because some developing waves tend to show vertically aligned structures at these
Stratospheric Sounding Unit (SSU), MSU, and AMSU-A; total column water vapor and surface wind speeds from SSM/I; ocean wave height and surface wind from ERS-1 and ERS-2 , total column ozone from the Total Ozone Mapping Spectroradiometer (TOMS), and ozone profiles from SBUV] ( Uppala et al. 2005 ). ERA-Interim is the newest generation of ECMWF reanalyses ( Simmons et al. 2006 ). Unlike ERA-40, which was limited to a 45-yr period (September 1957–August 2002; Uppala et al. 2005 ), ERA-Interim has near
Stratospheric Sounding Unit (SSU), MSU, and AMSU-A; total column water vapor and surface wind speeds from SSM/I; ocean wave height and surface wind from ERS-1 and ERS-2 , total column ozone from the Total Ozone Mapping Spectroradiometer (TOMS), and ozone profiles from SBUV] ( Uppala et al. 2005 ). ERA-Interim is the newest generation of ECMWF reanalyses ( Simmons et al. 2006 ). Unlike ERA-40, which was limited to a 45-yr period (September 1957–August 2002; Uppala et al. 2005 ), ERA-Interim has near
model runs can also be hard to interpret since interacting errors have time to pervade all aspects of the simulation. In between the first time step and free model climatology lies the process of error growth and spread, which may yield long-term errors quite different from the original source of the error (e.g., Rodwell and Jung 2008 ). At still longer lead times, other coupled model components (land and ocean) can further evolve the errors (e.g., Song and Mapes 2012 ). A “seamless” suite of data
model runs can also be hard to interpret since interacting errors have time to pervade all aspects of the simulation. In between the first time step and free model climatology lies the process of error growth and spread, which may yield long-term errors quite different from the original source of the error (e.g., Rodwell and Jung 2008 ). At still longer lead times, other coupled model components (land and ocean) can further evolve the errors (e.g., Song and Mapes 2012 ). A “seamless” suite of data
ocean evaporation Yu and Weller (2007) ]. Ultimately, activities such as hydrologic applications would like to make use of reanalyses, but the accuracy of model physics that control the fluxes requires further development (e.g., Maurer et al. 2001 ). The physical terms of the reanalysis budgets generally do not balance even over long periods because the atmospheric data assimilation provides additional constraint (or forcing) in the balance of the output data. This is ultimately presented as a
ocean evaporation Yu and Weller (2007) ]. Ultimately, activities such as hydrologic applications would like to make use of reanalyses, but the accuracy of model physics that control the fluxes requires further development (e.g., Maurer et al. 2001 ). The physical terms of the reanalysis budgets generally do not balance even over long periods because the atmospheric data assimilation provides additional constraint (or forcing) in the balance of the output data. This is ultimately presented as a
1. Introduction Intraseasonal variability (ISV) of tropical convection (e.g., Madden and Julian 1972 , 1994 ; Lau and Chan 1986 ; Stephens et al. 2004 ) remains a compelling and enigmatic science target for several reasons. First, the role of various physical processes that govern organization of tropical convection remains unclear. Among the key mechanisms that have been invoked as crucial to the existence, organization, and pacing of ISV are variants of wave-conditional instability of the
1. Introduction Intraseasonal variability (ISV) of tropical convection (e.g., Madden and Julian 1972 , 1994 ; Lau and Chan 1986 ; Stephens et al. 2004 ) remains a compelling and enigmatic science target for several reasons. First, the role of various physical processes that govern organization of tropical convection remains unclear. Among the key mechanisms that have been invoked as crucial to the existence, organization, and pacing of ISV are variants of wave-conditional instability of the
, can be found in standard texts ( Stull 1988 ; Kraus and Businger 1994 ). Models vary in the application of surface parameterizations through the choice of several parameters such as roughness lengths, inclusion of ocean-wave effects, and the use of stability functions. Improvements in modeled fluxes can be made through improved estimation of the near-surface variables and/or the improved parameterization of these parameters that effectively control the transfer rates of momentum, heat, and
, can be found in standard texts ( Stull 1988 ; Kraus and Businger 1994 ). Models vary in the application of surface parameterizations through the choice of several parameters such as roughness lengths, inclusion of ocean-wave effects, and the use of stability functions. Improvements in modeled fluxes can be made through improved estimation of the near-surface variables and/or the improved parameterization of these parameters that effectively control the transfer rates of momentum, heat, and
evidenced by the results of Trenberth et al. (2009) and Bosilovich et al. (2011) who highlight uncertainties in water and energy fluxes among the most recent reanalysis efforts. For example, net downward energy flux at the surface over oceans is approximately 14 W m −2 for MERRA (but −18 W m −2 for JRA) over the period 2000–04 when modern remote sensing measurements should have the greatest impact. Global precipitation trends in MERRA exceed 0.5 mm day −1 or nearly 20% of the climatological
evidenced by the results of Trenberth et al. (2009) and Bosilovich et al. (2011) who highlight uncertainties in water and energy fluxes among the most recent reanalysis efforts. For example, net downward energy flux at the surface over oceans is approximately 14 W m −2 for MERRA (but −18 W m −2 for JRA) over the period 2000–04 when modern remote sensing measurements should have the greatest impact. Global precipitation trends in MERRA exceed 0.5 mm day −1 or nearly 20% of the climatological
