Selecting Suitable Variables for Numerical Prediction Models of New England NE Winter Hydroclimate NOAA Climate Predict

1. Selecting Suitable Variables for Numerical Prediction Models of New England (NE) Winter HydroclimateNOAA Climate Prediction Assessments WorkshopAlexandria, 28-30 October 2002 James A. Bradbury Cameron P. Wake and Barry D. Keim NOAA funded through the AIRMAP project (Atmospheric Investigation, Regional Mapping, Analysis and Prediction)

2. Overview Brief summary of NE winter synoptic climatology Precipitation Introduce possible predictors/ relevant teleconnection patterns North Atlantic Oscillation (NAO) Regional Sea Surface Temperatures (SST) El Ni�o/ Southern Oscillation (ENSO) Discuss mechanisms for observed statistical links Regional atmospheric circulation and cyclone activity

4. NE Precipitation General regions of heavy and light precipitation relative to typical mid-tropospheric flow East-west shifting of the East Coast pressure trough is related to NE precipitation variability �Rossby wave� position and precipitation regimes are largely a function of geography but character and location of waves vary on monthly and longer time-scales �Rossby wave� position and precipitation regimes are largely a function of geography but character and location of waves vary on monthly and longer time-scales

5. The NAO Index Defines the steepness of the monthly N-S Sea-level Pressure gradient across the NE Atlantic (Mostly in the winter) Positive NAO Stronger circumpolar vortex Warmer Northern Hemisphere continents Negative NAO Weaker circumpolar vortex Frequent �blocking� episodes

9. Previous Studies: Northeastern U.S. Climate and NAO Positive NAO Winter Warmer air and SS Temps Less Snow More Streamflow Negative NAO Winter Colder air and SS Temps More snow Less Streamflow

10. NAO and Winter Streamflow anomalies(correlation coefficients)

11. NAO and Winter Streamflow Most significant correlations occur on decadal time-scales Suggestive of mechanisms? (hydrologic storage and SST) This is sweet!This is sweet!

12. Regional SST data Regions determined using rotated principal component analysis (RPCA) on winter season monthly gridded SST data (Smith et al., 1996) In all four seasons a significant loading pattern emerged in the NE region (Data from Suzanne Hartley�s web page)

13. Regional SST anomalies are highly persistent SST anomalies lag the Phase of the Winter NAO by 1 year Predictive Value of Regional SSTs (and the NAO)

14. December SST as a Seasonal Predictor Dec. SSTs with: Winter season (DJFM) 1) cyclone occurrences (top) 2) Vermont Streamflow (bottom)

15. Relations between regional SST and NE temperature and snowfall

17. Domain for Regional Synoptic Index Based on subset of NCEP Reanalysis global grid (2.5� x 2.5�) Monthly 500-hPa pressure level data (1948-1998)

18. Trough Axis Index (TAI) Estimate trough location (in longitudinal units) Monthly mean location of the minimum 500-hPa heights from 4 latitudinal steps (40�, 42.5�, 45�, 47.5� North)

19. New index that describes the mean monthly location of the East Coast trough (14 missing months) Useful for comparison with indices for regional synoptic- to hemisphere-scale circulation

20. Correlations between the Trough Axis Index and Regional Precipitation

21. Time-series of the winter averaged TAI, Cavendish precipitation, and regional SSTs Multi-annual and decadal trends in winter precipitation show good agreement with the TAI and regional SSTs

23. Cyclone Dataset Cyclone occurrences cyclogenesis cyclolysis

24. The first two modes of regional winter Cyclone (CYN), Precipitation (PPT) and Snowfall (SNW) variability Percent variance in cyclone occurrence (PPT and SNW) explained by each eigenmode, at each grid point. Percent of overall variance explained by each mode is shown in parentheses. Percent variance in cyclone occurrence (PPT and SNW) explained by each eigenmode, at each grid point. Percent of overall variance explained by each mode is shown in parentheses.

26. The main modes of winter hydroclimate and cyclone variability with relevant predictor variables

27. Composite of cyclone occurrences during winter months (DJFM) with high positive NAO index values (top 25th percentile, n = 42) minus cyclone occurrences during winter months with low negative index values (bottom 25th percentile, n = 41) (Jan. 1958-March 1998). Contour interval = 10, positive contour lines are solid, negative contours are dashed and the zero contour interval is solid bold.Composite of cyclone occurrences during winter months (DJFM) with high positive NAO index values (top 25th percentile, n = 42) minus cyclone occurrences during winter months with low negative index values (bottom 25th percentile, n = 41) (Jan. 1958-March 1998). Contour interval = 10, positive contour lines are solid, negative contours are dashed and the zero contour interval is solid bold.

28. Composite of cyclone occurrences during winter months with above average NE SSTs minus cyclone occurrences during winter months with below average NE SSTs (Dec 1958-March 1992). Contour interval = 10, positive contour lines are solid, negative contours are dashed and the zero contour interval is solid bold. Composite of cyclone occurrences during winter months with above average NE SSTs minus cyclone occurrences during winter months with below average NE SSTs (Dec 1958-March 1992). Contour interval = 10, positive contour lines are solid, negative contours are dashed and the zero contour interval is solid bold.

29. Summary Conceptual model for Dry winters Eastward shifted trough (high TAI) Below normal SSTs Storms tracking off-shore These conditions are also associated with a negative NAO Mostly relevant inland

30. Conclusions Predictors for Regional Streamflow NE Regional SSTs AO/ NAO/ Stratospheric vortex intensity Preceding month�s streamflow (ENSO) Predictors for Regional Snowfall NE Regional SSTs (for coastal sites) Still uncertain for inland sites (ENSO?) Work-To-Do Develop most effective multivariate prediction model Include other parameters (indices for future climate change?) Long-term goals Use empirical model to aid in the development of a dynamical regional climate model

31. Questions?