1. Large-Scale Regime Transition and Its Relationship to Significant Cool Season Precipitation Events in the Northeast Heather Archambault Advisors: Lance Bosart and Daniel Keyser NWS Focal Point: Rich Grumm Department of Earth and Atmospheric Sciences, University at Albany, State University of New York

2. Research Motivation Meteorological wisdom: increased threat of major storm during large-scale regime changeMeteorological wisdom: increased threat of major storm during large-scale regime change Past research points to a possible connection between synoptic-scale cyclogenesis and reconfiguration of the planetary-scale flowPast research points to a possible connection between synoptic-scale cyclogenesis and reconfiguration of the planetary-scale flow Dave Groenert (CSTAR, 2002) documented an apparent tendency for an increased number of precipitation events in the Northeast during phase transitions of the NAODave Groenert (CSTAR, 2002) documented an apparent tendency for an increased number of precipitation events in the Northeast during phase transitions of the NAO

3. Presentation Overview Research goalsResearch goals Climatology of PNA/NAO tendencyClimatology of PNA/NAO tendency Time series of NAO/PNA tendency [d(INDEX)/dt]Time series of NAO/PNA tendency [d(INDEX)/dt] –Correlations with NE domain-average daily precipitation –Correlations with 1000 hPa NE Height Anomaly Index (HAI) Major regime change / NE precipitation correlationMajor regime change / NE precipitation correlation Major regime change / NE height anomaly correlationMajor regime change / NE height anomaly correlation Future workFuture work

4. Focus of Research: Review literature that documents individual cases of reconfiguration of the planetary-scale flow in association with major precipitation events in the Northeastern United StatesReview literature that documents individual cases of reconfiguration of the planetary-scale flow in association with major precipitation events in the Northeastern United States Relate previous work to modern definitions of regime changeRelate previous work to modern definitions of regime change Determine and quantify an objective definition for a significant large-scale regime changeDetermine and quantify an objective definition for a significant large-scale regime change –Phase change of teleconnection index (PNA, NAO) greater than 2 standard deviations over a 7-day period

5. Focus (cont): Determine whether more storms/precipitation can be expected during regime changes as compared to climatology in the NortheastDetermine whether more storms/precipitation can be expected during regime changes as compared to climatology in the Northeast Construct composite analyses to identify characteristic signatures of significant large-scale regime changesConstruct composite analyses to identify characteristic signatures of significant large-scale regime changes Use composite analyses and results from case studies to determine whether possible regime change/precipitation relationships are associative or cause and effectUse composite analyses and results from case studies to determine whether possible regime change/precipitation relationships are associative or cause and effect

6. Positive PNA – Thickness Anomalies Courtesy: Anantha Aiyyer & Eyad Atallah

7. Negative PNA – Thickness Anomalies Courtesy: Anantha Aiyyer & Eyad Atallah

8. Positive NAO – Thickness Anomalies Courtesy: Anantha Aiyyer & Eyad Atallah

9. Negative NAO – Thickness Anomalies Courtesy: Anantha Aiyyer & Eyad Atallah

10. NAO Tendencies Climatology of Teleconnection Tendencies PNA Tendencies (1948-2001, 7 Day Interval)

11. Daily Index Tendency / NE Precipitation Time Series Correlations Daily time series of NAO/PNA tendency were createdDaily time series of NAO/PNA tendency were created –Seven-day tendency: Index on Day (t + 3) – Index on Day (t - 3) Correlated with time series of daily NE precipitationCorrelated with time series of daily NE precipitation –Standardized domain-average precipitation values calculated from the Unified Precipitation Dataset (UPD) from 1954 - 1998 Seven-day NAO tendency / precipitation correlation:Seven-day NAO tendency / precipitation correlation: –Correlation Coefficient: -0.04, R 2 value: 0.0 Seven-day PNA tendency / precipitation correlation:Seven-day PNA tendency / precipitation correlation: –Correlation Coefficient: 0.04, R 2 value: 0.0 Conclusion: No correlation between daily teleconnection index tendency and Northeast daily precipitation valuesConclusion: No correlation between daily teleconnection index tendency and Northeast daily precipitation values

12. 1000 hPa Height Anomaly Index (HAI) Purpose: correlate with daily NAO/PNA tendency as an alternative to daily precipitation valuesPurpose: correlate with daily NAO/PNA tendency as an alternative to daily precipitation values 1000 hPa height anomalies calculated from NCEP/NCAR Reanalysis (1951 – 2001)1000 hPa height anomalies calculated from NCEP/NCAR Reanalysis (1951 – 2001) Height anomalies are normalized according to climatology of the 15-day period centered around each dayHeight anomalies are normalized according to climatology of the 15-day period centered around each day This accounts for the higher mean/smaller standard deviation in the height field that exists in summer vs. winterThis accounts for the higher mean/smaller standard deviation in the height field that exists in summer vs. winter

14. 48.2 % of days had HAI < 0 (8,970 of 18,628 days)

15. Daily Index Tendency / NE Height Anomaly Time Series Correlations Time series of daily tendency of the NAO/PNA were correlated with time series of daily 1000 hPa Northeast Height Anomaly Index (HAI)Time series of daily tendency of the NAO/PNA were correlated with time series of daily 1000 hPa Northeast Height Anomaly Index (HAI) Seven-day NAO tendency / HAI correlation:Seven-day NAO tendency / HAI correlation: –Correlation Coefficient: -0.06, R 2 value: 0.00 Seven-day PNA tendency / HAI correlation:Seven-day PNA tendency / HAI correlation: –Correlation Coefficient: -0.23, R 2 value: 0.05 Conclusion: No significant correlation between daily index tendency and daily Northeast 1000 hPa height anomaly indexConclusion: No significant correlation between daily index tendency and daily Northeast 1000 hPa height anomaly index

16. Major Regime Change / NE Precipitation Correlations Based on definition of major regime change: created a subset of days for further correlations with precipitationBased on definition of major regime change: created a subset of days for further correlations with precipitation Index change during major regime transition was correlated with daily precipitationIndex change during major regime transition was correlated with daily precipitation –Major regime transition: phase change of greater than 2 standard deviations

17. Overall NAO regime change / precipitation correlation: Correlation Coefficient: -0.07, R 2 value: 0.01

18. Overall PNA regime change / precipitation correlation: Correlation Coefficient: 0.11, R 2 value: 0.01

19. Major Regime Change / 1000 hPa NE Height Anomaly Correlations Index tendency for the days where a major regime change was ongoing was correlated with daily 1000 hPa height anomaliesIndex tendency for the days where a major regime change was ongoing was correlated with daily 1000 hPa height anomalies

20. Overall NAO regime change / HAI correlation: Correlation Coefficient: -0.15, R 2 value: 0.02

21. Overall PNA regime change / HAI correlation: Correlation Coefficient: 0.0, R 2 value: 0.0

22. Preliminary Results No correlation between daily time series of index tendency & NE precipitation / 1000 hPa height anomaliesNo correlation between daily time series of index tendency & NE precipitation / 1000 hPa height anomalies Major NAO swings / NE precipitation:Major NAO swings / NE precipitation: –Weakening jet (+NAO to –NAO) : possible enhanced precipitation in winter –Strengthening jet (–NAO to +NAO): possible enhanced precipitation in summer For major swings in the NAO, a slight negative correlation with 1000 hPa height anomalies can be found in the summer months:For major swings in the NAO, a slight negative correlation with 1000 hPa height anomalies can be found in the summer months: – negative height anomalies are correlated with a strengthening of the North Atlantic jet (negative to positive NAO) – approximately 10% of height anomaly variability can be attributed to major swings in the NAO during summer months

23. Preliminary Results (cont.) For major swings in the PNA, the largest correlation with precipitation can be found in March (R 2 = 0.29) and AprilFor major swings in the PNA, the largest correlation with precipitation can be found in March (R 2 = 0.29) and April –Ridge building in west/troughing in SE: enhanced precipitation No overall correlation between major swings in the PNA and 1000 hPA height anomaliesNo overall correlation between major swings in the PNA and 1000 hPA height anomalies –Slight positive correlation is found in May-June PNA change from positive to negative corresponds with heights lowering in western Canada/Pacific NW, ridge amplification over SE USPNA change from positive to negative corresponds with heights lowering in western Canada/Pacific NW, ridge amplification over SE US –Slight negative correlation is found in July-August PNA change from negative to positive corresponds with building ridge in western Canada/Pacific NW and lower heights in the southeastern USPNA change from negative to positive corresponds with building ridge in western Canada/Pacific NW and lower heights in the southeastern US

24. What’s Next Determine whether a “significant” precipitation event is more likely during a major PNA or NAO swing as compared to climatologyDetermine whether a “significant” precipitation event is more likely during a major PNA or NAO swing as compared to climatology –Lag correlations: storm at end, beginning of regime change? Create correlation maps using top NAO and PNA transitionsCreate correlation maps using top NAO and PNA transitions –Find best correlation between transitions and 1000 hPa height anomalies/precipitation for the United States