1. Rossby Wave Breaking and Blocking in Subseasonal Simulations
Rainer Bleck1,2, Shan Sun1,2, and Stanley G. Benjamin1
1NOAA • Earth System Research Laboratory • Global Systems Division
2University of Colorado, Cooperative Institute for Research in Environmental Sciences
Results
Introduction
Fig.2: Geographic distribution of the WB wave-breaking index (left) and the PH blocking index (right) in 32-day simulations, averaged over all model runs. PH sampling interval: ±120 rel. to mean jet axis.
Numerical models used in extended-range weather prediction must be able to faithfully represent the occasional appearance of near-stationary, meridionally aligned cyclone-anticyclone pairs that act as barriers to mid-latitude flow. Here we investigate this “blocking” phenomenon in the context of Rossby wave breaking. The tools used in this study are
the Flow-following Icosahedral Model FIM [1] ( coupled to an icosahedral variant of the ocean model HYCOM.
Daily maps of potential temperature (𝜃) on a tropopause-level potential vorticity (PV) surface, based on weekly 4-member 32-day FIM simulations at 60km resolution over the period
Algorithms for detecting blocking and Rossby wave breaking events.
Fig.3: Geographic distribution of the WB wave-breaking index during week 1 (left) and week 4 (right) averaged over all model runs.
Blocking indices: Pelly-Hoskins [2] and Tibaldi-Molteni [4]. To focus on weather-relevant, longer-term blocking episodes, a “longevity” filter is applied to both indices.
Wave breaking index: based on the total variation of 𝜃,
in a region defined as follows. On each meridian 𝜆, the region extends from latitude 𝜑1 where 𝜃 starts increa-sing poleward (it normally decreases) to latitude 𝜑2 where 𝜃 is back to its starting value. The longitude range extends over the contiguous region in which an interval (j1,j2) as defined above exists (Fig.1). The WB index captures both geographic extent and amplitude of the breaking wave. Related to the index used in [3].
Fig.4: Geographic distribution of the PH blocking index during week 1 (left) and week 4 (right) averaged over all model runs. PH sampling interval: ±120 rel. to mean jet axis.
𝑊𝐵= | 𝜕𝜃 𝜕𝜑 | 𝑑𝜑 𝑑𝜆,
Fig.5: Blocking frequency based on the TM index, plotted against longitude. Left: FIM; right: CFSv2.
Fig.6: Geographic distribution of the PH blocking index in higher-viscosity simulations. Left: week 4 (compare to Fig.4 right). Right: all lead times (compare to Fig.2 right).
Discussion
Breaking Rossby waves occasionally molt into blocks, but blocks typically do not form in regions where Rossby waves break most often (Fig.2).
FIM shows some decline in wave breaking and PH blocking frequency (24% and 9%, resp.) from week 1 to week 4 (Figs. 3 and 4).
In contrast, CFSv2 shows a slight increase in blocking frequency with lead time (Fig.5), based on the TM index. (No detailed 𝜃 𝑃𝑉 maps are available for CFSv2, hence the switch to TM for model intercomparison).
A 10-fold increase in lateral viscosity has no perceptible effect on the frequency of blocking (Fig.6).
Fig.1: Sample plot of 𝜃 [K] on a tropopause-level PV surface. Two regions identified by the WB index as Rossby wave breaking events highlighted in black.
REFERENCES:
[1] Bleck, R. and co-workers, 2015: A Vertically Flow-Following Icosahedral Grid Model for Medium-Range and Seasonal Prediction. Part I: Model Description. Mon. Wea. Rev., 143,
[2] Pelly, J. L., and B. J. Hoskins, 2003: A new perspective on blocking. J. Atm. Sci., 60, 743–755
[3] Strong, C., and G. Magnusdottir, 2008: Tropospheric Rossby Wave Breaking and the NAO/NAM. J. Atm. Sci, 65,
[4] Tibaldi, S., and F. Molteni, 1990: On the operational predictability of blocking. Tellus, 42A,