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

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

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

West Mountain Great Plains Midwest East Study Area

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

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

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

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

Results Identification of Singularities December 25

Results Identification of Singularities December 26

Results Identification of Singularities December 27

Results Identification of Singularities December 28

Results Identification of Singularities December 29

Results Identification of Singularities December 30

Results Identification of Singularities December 31

Results Identification of Singularities January 1

Results Identification of Singularities January 2

Results Identification of Singularities January 3

Results Identification of Singularities January 4

Results Identification of Singularities January 5

Results Identification of Singularities January 6

Results Identification of Singularities January 7

Results Identification of Singularities January 8

Results Identification of Singularities January 9

Results Identification of Singularities January 10

Results Identification of Singularities January 11

Results Identification of Singularities January 12

Results Identification of Singularities January 13

Results Identification of Singularities January 14

Results Identification of Singularities January 15

Results Identification of Singularities January 16

Results Identification of Singularities January 17

Results Identification of Singularities January 18

Results Identification of Singularities January 19

Results Identification of Singularities January 20

Results Identification of Singularities January 21

Results Identification of Singularities January 22

Results Identification of Singularities January 23

Results Identification of Singularities January 24

Results Identification of Singularities January 25

Results Identification of Singularities January 26

Results Identification of Singularities January 27

Results Identification of Singularities January 28

Results Identification of Singularities January 29

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

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 +

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

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

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