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REFERENCES: [1]  Bleck , R. and co-workers, 201 REFERENCES: [1]  Bleck , R. and co-workers, 201

REFERENCES: [1] Bleck , R. and co-workers, 201 - PowerPoint Presentation

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REFERENCES: [1] Bleck , R. and co-workers, 201 - PPT Presentation

5 A Vertically FlowFollowing Icosahedral Grid Model for MediumRange and Seasonal Prediction Part I Model Description Mon Wea Rev 143 23862403 2 Pelly J L and B J Hoskins 2003 A new perspective on blocking J ID: 713077

fig blocking breaking index blocking fig index breaking wave model week rossby left fim geographic distribution simulations frequency based

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Slide1

REFERENCES:

[1]

Bleck

, R. and co-workers, 201

5:

A Vertically Flow-Following Icosahedral Grid Model for Medium-Range and Seasonal Prediction. Part I: Model Description. Mon. Wea. Rev., 143, 2386-2403.[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, 2861-2876.[4] Tibaldi, S., and F. Molteni, 1990: On the operational predictability of blocking. Tellus, 42A, 343-365.

Rossby

Wave Breaking and Blocking in Subseasonal Simulations

Rainer Bleck1,2, Shan Sun1,2, and Stanley G. Benjamin11NOAA • Earth System Research Laboratory • Global Systems Division2University of Colorado, Cooperative Institute for Research in Environmental Sciences

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 arethe Flow-following Icosahedral Model FIM [1] (http://fim.noaa.gov) coupled to an icosahedral variant of the ocean model HYCOM.Daily maps of potential temperature on a tropopause-level potential vorticity (PV) surface, based on weekly 4-member 32-day FIM simulations at 60km resolution over the period 1999-2014.Algorithms for detecting blocking and Rossby wave breaking events.

 

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 in a region defined as follows. On each meridian the region extends from latitude 1 where starts increa-sing poleward (it normally decreases) to latitude 2 where 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.1: Sample

plot of

[K] on a tropopause-level PV surface. Two regions identified by the WB index as Rossby wave breaking events highlighted in black.

 

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.

 

Fig.5: Blocking frequency based on the TM index, plotted against longitude. Left: FIM; right: CFSv2.

Results

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 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.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).

Fig.3: Geographic distribution of the WB wave-breaking index during week 1 (left) and week 4 (right) averaged over all model runs.

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.