Search Results
response of the model because of a slow cooling trend in an unforced or control simulation ( Watterson and Dix 2005 ). The warming trend over 22C is 0.3 K (100 yr) −1 (again, an underestimate). The latitudinal distribution of the warming in the SH is shown in Fig. 15a . In the annual result there is a minimum in warming in the Southern Ocean, although this varies seasonally (in part due to effects of sea ice). The changes in SLP are shown in Fig. 15b . For each season, the pressure decreases near
response of the model because of a slow cooling trend in an unforced or control simulation ( Watterson and Dix 2005 ). The warming trend over 22C is 0.3 K (100 yr) −1 (again, an underestimate). The latitudinal distribution of the warming in the SH is shown in Fig. 15a . In the annual result there is a minimum in warming in the Southern Ocean, although this varies seasonally (in part due to effects of sea ice). The changes in SLP are shown in Fig. 15b . For each season, the pressure decreases near
: generation of gravity waves, vorticity, and potential vorticity (PV) in the wakes of mountains (see Schär 2002 for a review); global angular momentum balance (which seasonally trades between atmosphere and solid earth through the action of pressure and frictional drag, leading to a 1-ms annual variation in the length of the day; e.g., Hide et al. 1997 ); β -plane wake and dynamic blocking effects (e.g., Rhines 2007 ); and studies of vertical structure, notably “barotropization” ( Rhines 1979 ). 2
: generation of gravity waves, vorticity, and potential vorticity (PV) in the wakes of mountains (see Schär 2002 for a review); global angular momentum balance (which seasonally trades between atmosphere and solid earth through the action of pressure and frictional drag, leading to a 1-ms annual variation in the length of the day; e.g., Hide et al. 1997 ); β -plane wake and dynamic blocking effects (e.g., Rhines 2007 ); and studies of vertical structure, notably “barotropization” ( Rhines 1979 ). 2
climatological seasonal cycle, SFW events accelerate the annual weakening of high-latitude circumpolar westerlies simultaneously at stratospheric and tropospheric altitudes. This behavior is manifested by a coherent pattern of westerly (easterly) annular zonal wind anomalies extending from the midstratosphere to the earth’s surface at high latitudes prior to (after) SFW events, coinciding with the polar vortex breakdown. BMR ’s results suggest that SFW events are associated with a robust large-scale coupling
climatological seasonal cycle, SFW events accelerate the annual weakening of high-latitude circumpolar westerlies simultaneously at stratospheric and tropospheric altitudes. This behavior is manifested by a coherent pattern of westerly (easterly) annular zonal wind anomalies extending from the midstratosphere to the earth’s surface at high latitudes prior to (after) SFW events, coinciding with the polar vortex breakdown. BMR ’s results suggest that SFW events are associated with a robust large-scale coupling
central dates). 5) Climatology and anomalies For every variable, we have removed the respective seasonal cycle, which was estimated by computing at each day the respective interannual mean and then by smoothing the obtained time series of daily interannual means with a 31-day running average. Daily values of the energy for the total circulation (i.e., climatology + anomaly field) were computed before the seasonal cycle was removed. On the other hand since we intend to analyze composites of the daily
central dates). 5) Climatology and anomalies For every variable, we have removed the respective seasonal cycle, which was estimated by computing at each day the respective interannual mean and then by smoothing the obtained time series of daily interannual means with a 31-day running average. Daily values of the energy for the total circulation (i.e., climatology + anomaly field) were computed before the seasonal cycle was removed. On the other hand since we intend to analyze composites of the daily
intraseasonal variability to zonally asymmetric forcing. Here, Δ T eq is perhaps the most physical parameter, and can be linked to changes in forcing during the seasonal cycle, or changes to the climate, for example, a reduction of the ice–albedo effect due to global warming may reduce the equator-to-pole temperature contrast. Figure 3 summarizes the results from experiment sets I and V–VIII. For each experiment we compare the e -folding time scale of variability in three simulations in which the
intraseasonal variability to zonally asymmetric forcing. Here, Δ T eq is perhaps the most physical parameter, and can be linked to changes in forcing during the seasonal cycle, or changes to the climate, for example, a reduction of the ice–albedo effect due to global warming may reduce the equator-to-pole temperature contrast. Figure 3 summarizes the results from experiment sets I and V–VIII. For each experiment we compare the e -folding time scale of variability in three simulations in which the
” with systematic mean effects (also important in, e.g., biological molecular motors). By its very nature, a wave propagation mechanism such as the Rossby wave mechanism will organize the fluctuating fields, no matter how chaotic they may seem, in the sense of inducing systematic correlations among them. The correlations are shaped by the waves’ polarization relations and usually give rise to long-range stresses. They may produce phenomena like planetary equatorial superrotation, or gyroscopically
” with systematic mean effects (also important in, e.g., biological molecular motors). By its very nature, a wave propagation mechanism such as the Rossby wave mechanism will organize the fluctuating fields, no matter how chaotic they may seem, in the sense of inducing systematic correlations among them. The correlations are shaped by the waves’ polarization relations and usually give rise to long-range stresses. They may produce phenomena like planetary equatorial superrotation, or gyroscopically
