Department of Geological and Atmospheric Sciences 2nd RASM Workshop 2012 Spring Brandon J. Fisel Iowa State University Department of Geological and Atmospheric Sciences Monterey, CA 14-18 May

overview Sea ice – atmosphere interactions recent, rapid sea-ice decline Model circulation regimes exist Goal sea-ice area changes affects persistent circulation regimes implications for predictability of future atmospheric circulation seasonal T extremes Interactions between sea ice and arctic atmospheric circulation is of great interest to the scientific community because of sea-ice decline. Model circulation regimes exist. As sea-ice area changes, this affects the persistence of model dynamic circulation regimes which have implications for the predictability of future atmospheric circulation. 2nd RASM May 2012

methodology WRF-ARW 3.1 CORDEX domain 50 km resolution Analysis region Physical parameterizations appropriate for the Arctic provided by Cassano et al., 2011 and M. Seefeldt (post-doc at Colorado), collaborator, who extensively tested model skill parameterizations and groups of parameterizations in the Arctic. Polar modifications include the changes made and accepted by Hines et al., which includes fractional sea-ice concentrations. Coordinated Regional Downscaling Experiment (CORDEX). CORDEX domain is large enough so that the polar vortex is not constrained by the lateral boundary conditions. The red triangle represents the area where sea-ice concentrations were fractional in the fractional ensemble and binary in the binary ensemble. Area outside of the red triangle had binary sea ice in all simulations. During the simulation period, the largest area of fractional sea-ice concentrations occurred in the area of the red triangle allowing the largest differences between binary/fractional simulations. Analysis region 2nd RASM May 2012

methodology (cont…) ECMWF (ERA-Interim); 6-hr 1 Ensemble; 16 members SST and sea ice; prescribed; 12-hr 1 Ensemble; 16 members June – December, 2007 1 or 2 regimes Model-based clustering model selection; BIC model fit; penalty for model complexity Time windows > synoptic time frame; 7 & 11 days Atmospheric forcing data made available from ECMWF every 6 hours at T255 resolution. Sea-ice concentrations of 10% or less set to 0% to account for retrieval errors and potential melt ponding (melt ponds, especially in summer have an open-ocean signature and it is difficult to distinguish between ice-covered and open-ocean). Ensemble members were staggered 12 hours apart and were spun-up for 2 weeks so they would be independent of their initial conditions and reach distinctly different atmospheric states by June 15, when we began our analysis. We looked for when 1 or 2 regimes occurred in our target region using model-based clustering, a common technique for indentifying similar components of multivariate data. Model selection provided by the BIC parameter, which provides a summary of model fit and includes a penalty for the number of parameters in the model. The BIC determines when 1 or more regimes is favored for time windows, 7 and 11 days. Circulations regimes that persist longer than synoptic time frame so that a new synoptic state can enter into the region. Any one day could have two regimes and is expected to persist as long as any 500 hPa state. 2nd RASM May 2012

methodology (cont…) Seasonal T extremes JJA and OND; 1st and 99th percentile regions; ANC, AS1 and SC land points only ANC SC AS1 2nd RASM May 2012

regimes 1 regime 2 regimes; significantly separated Regimes were common and behavior made up of 1/2 separated/2 trends. When some members trend towards higher pressure and others trend towards lower pressure. 2 regimes; differing trends 2nd RASM May 2012

regimes (cont…) significantly separated high MSLP low MSLP differing trends c) high MSLP d) low MSLP Regimes were common and behavior made up of 1/2 separated/2 trends. When some members trend towards higher pressure and others trend towards lower pressure. 2nd RASM May 2012

regimes (cont…) Model variability Month 75th – 25th [hPa] June(15-30) 6.1 July 4.9 August 8.1 September 5.7 October 10.1 November 9.0 December 9.8 < 8 hPa I first wanted to understand when model variability was small in our simulations. Inter-quartile differences (75th - 25th) of daily MSLP. Wider range of variability for OND than JJA and September. Supports the concept that there is a difference in summer and winter circulations and that the summer circulation evolves more slowly than the winter circulation (could be due to weaker temperature differences, which would result in a weaker jet stream). Assuming the distribution is normally distributed, the IQ difference amounts to 50% of the data and isn’t affected by outliers. > 8 hPa 2nd RASM May 2012

regimes (cont…) BIC2 > BIC1 Month 7-Day 11-Day June(15-30) 93% 100% July 71% 87% August 84% 97% September 33% 30% October 74% 94% November 63% 83% December 96% We wanted to know how common multiple regimes were in our simulations. Percentage of days when BIC prefers 2 regimes to 1 regimes. Point out slight tendency of multiple regimes preferred in JJA than OND, but JJA and OND are similar in that there are multiple regimes common for both periods. Point out that the largest difference occurred in September during the sea-ice minimum. 2nd RASM May 2012

regimes (cont…) Regime streaks JJA: tendency to remain in one mode of regime behavior Slower evolving atmospheric flow Season Mean length [days] JJA (7-Day) 7.9 JJA (11-Day) 24.0 OND (7-Day) 6.0 OND (11-Day) 15.0 Knowing when model variability is small and that multiple regimes were common in our simulations, we wanted to understand the persistence of multiple regimes in our simulations, so we computed regime streaks (consecutive days) when multiple regimes are favored to 1 regime. The persistence of multiple regimes to be preferred to 1 regime during JJA vs. OND suggests that the model has a tendency to remain in one mode of regime behavior during JJA. This also suggests that the atmospheric flow during summer evolves more slowly. 2nd RASM May 2012

regimes (cont….) 2-Regime JJA 2-Regime OND How persistent do days fall into a 1 or 2-regime window? 2-Regime OND More persistent days a calendar date falls into a 2-regime window. The number of days a calendar date falls into a 2-regime (7-day) window binned by season (JJA & OND). A calendar date may fall into a 2-regime window as it moves in time from 0x to 7x, with 7x describing very long periods of persistent 2-regime behavior. 2nd RASM May 2012

extremes 2-Regime JJA [1st P] 2-Regime OND [99th P] Cold extremes preferred in 2-regime behavior. 2-Regime OND [99th P] Warm extremes preferred in 1-regime behavior. 2nd RASM May 2012

summary Multiple dynamic regimes Implications persistent, multiple regimes (JJA) less persistent, multiple regimes (OND) persistent, 1-regime (September) Implications changing sea-ice area affects regime persistence future predictability of atmosphere future decline in summer sea ice more persistent regime behavior? More persistent multiple regimes in JJA than in OND, which saw less persistent multiple regimes. The persistence in JJA may be due to specified SST’s, preventing ocean-atmosphere interactions and constraining the variability of the model’s atmospheric circulation. OND, which has a larger interactive ice surface, may allow for more variability in the model’s atmospheric circulation. September witnessed the largest change in regime behavior, whereby there was more persistent 1-regime behavior, which may be due to a large open-ocean area that may greatly constrain the variability of the model’s atmospheric circulation, allowing for more persistent regime behavior. This work suggests that changing sea-ice area affects the predictability of atmospheric circulation, which has implications on the future predictability of atmospheric circulation. Many studies have suggested that future reductions in summer sea ice may continue, which may affect the persistence of multiple regimes, whereby the atmosphere is more predictable. Uncertainties due to prescribed SST’s that constrain model variability. 2nd RASM May 2012

summary (cont…) Temperature extremes Future work tendency for more cold (warm) extremes with 2(1)-regime behavior Future work regimes: coupled model; modeled vs. prescribed SST’s; similar behavior? T extremes: 20-year simulation (uncoupled WRF, RASM) change in energy/moisture budgets allow for a more extreme, future arctic? 2nd RASM May 2012

questions ? E-mail: bjfisel@gmail.com 2nd RASM May 2012

regimes Ensemble MSLP Model Estimated Mean 95% CI Shading is where about 95% of realizations from each regime should fall into, based on model fit.

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regimes 2-Regime September The number of days a calendar date falls into a 2-regime (7-day) window binned by season (JJA & OND). A calendar date may fall into a 2-regime window as it moves in time from 0x to 7x, with 7x describing very long periods of persistent 2-regime behavior. More persistent 1-regime behavior vs. JJA and OND. 2nd RASM May 2012

extremes 2-Regime Sept. [1st P] 2-Regime Sept. [99th P] Cold extremes preferred in 2-regime behavior. 2-Regime Sept. [99th P] Warm extremes preferred in 1-regime behavior. The number of days a calendar date falls into a 2-regime (7-day) window binned by season (JJA & OND). A calendar date may fall into a 2-regime window as it moves in time from 0x to 7x, with 7x describing very long periods of persistent 2-regime behavior. 2nd RASM May 2012