1. A Synoptic Climatological Approach to the Identification of January Temperature Anomalies in the United States Melissa Malin Katrina Frank Steven Quiring Richard Boutillier Laurence Kalkstein Center for Climatic Research Department of Geography University of Delaware

2. an anomalous warm spell that occurs during the coldest time of year a singularity: “…a characteristic meteorological condition that tends to occur on or near a specific calendar date.” ~ American Meteorological Society has roots in New England weather folklore discrepancies exist as to the timing of the singularity possible causal mechanisms include: –oceanic forcings (Hayden 1976) –atmospheric patterns (Wahl 1953) –extra-terrestrial events (sunspots, meteor showers) (Bowen 1956, Newman 1965) January Temperature Anomaly The January Thaw

3. identify winter temperature singularities across the United States and the inter- and intra- regional variability of the event(s) assess the potential that changes in air mass frequency are a causal mechanism for the event(s) Goal of the Project

4. West Mountain Great Plains Midwest East Study Area

5. Study Period December 1—February 28, 1948—2000 Air Temperature Data 4 a.m. + 4 p.m. Average Daily Air Temperature ~National Climatic Data Center Spatial Synoptic Classification Air Mass Data Dry Moderate (DM) / Dry Moderate + (DM+) Dry Polar (DP) / Dry Polar - (DP-) Dry Tropical (DT) Moist Moderate (MM) Moist Polar (MP) / Moist Polar + (MP+) Moist Tropical (MT) Transition (TR) Methods Data

6. daily average temperature data plotted for each station standardized using a five-day moving window Philadelphia, Pennsylvania Window Number Methods Windowing

7. Window Number second-order polynomial curve fit for winter trendline upper/ lower bounds set at two standard deviations Methods Identification of Singularities Philadelphia, Pennsylvania Winter Trendline Lower Bound Upper Bound singularity at January 24 -25

8. Cheyenne, Wyoming Freeze singularity at January 2- 4 Winter Trendline Lower Bound Upper Bound Thaw singularity at January 16-18 Methods Identification of Singularities example at Mountain Region station

9. Results Identification of Singularities December 25

10. Results Identification of Singularities December 26

11. Results Identification of Singularities December 27

12. Results Identification of Singularities December 28

13. Results Identification of Singularities December 29

14. Results Identification of Singularities December 30

15. Results Identification of Singularities December 31

16. Results Identification of Singularities January 1

17. Results Identification of Singularities January 2

18. Results Identification of Singularities January 3

19. Results Identification of Singularities January 4

20. Results Identification of Singularities January 5

21. Results Identification of Singularities January 6

22. Results Identification of Singularities January 7

23. Results Identification of Singularities January 8

24. Results Identification of Singularities January 9

25. Results Identification of Singularities January 10

26. Results Identification of Singularities January 11

27. Results Identification of Singularities January 12

28. Results Identification of Singularities January 13

29. Results Identification of Singularities January 14

30. Results Identification of Singularities January 15

31. Results Identification of Singularities January 16

32. Results Identification of Singularities January 17

33. Results Identification of Singularities January 18

34. Results Identification of Singularities January 19

35. Results Identification of Singularities January 20

36. Results Identification of Singularities January 21

37. Results Identification of Singularities January 22

38. Results Identification of Singularities January 23

39. Results Identification of Singularities January 24

40. Results Identification of Singularities January 25

41. Results Identification of Singularities January 26

42. Results Identification of Singularities January 27

43. Results Identification of Singularities January 28

44. Results Identification of Singularities January 29

45. Methods Air Mass Frequency Analysis Second-Order Polynomial Fit Bismarck, North Dakota Dry Polar - Bismarck, North Dakota Dry Polar - fit trendline to winter air mass frequency found differences to winter air mass trendline

46. Window Number correlated air mass frequency differences with temperature singularities | r | > 0.8 = strong correlation, 0.8  | r |  0.5 = moderate correlation, | r | < 0.5 = weak correlation Methods Air Mass Frequency Analysis Linear Fit Philadelphia, Pennsylvania Moist Polar + Philadelphia, Pennsylvania Moist Polar +

47. Results January Thaw Mountain no clear signal character, rather than frequency, of air masses may be changing? Plains increased DP and decreased DP- frequency suggests character change Midwest increased MT and decreased DT suggests circulation pattern change

48. Results January Freeze West increased polar frequency decreased moderate frequency Plains decreased DP and increased DP- frequency suggests character change

49. this research offers strong support for the existence of cohesive January Thaw and January Freeze events show signs of systematic movement across the United States –suggests potential of circulation as causal mechanism air mass analysis shows... Freeze associated with less frequent warm air masses, more frequent cold air masses Thaw not clearly associated with air mass frequency need for an investigation of air mass character and upper level flow patterns Conclusions and Directions for Future Research