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A Statistical Analysis and Synoptic Climatology of U.S. Heat Waves Scott C. Runyon and Lance F. Bosart Department of Earth and Atmospheric Sciences, University at Albany, State University of New York Introduction : Why study heat waves?

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a statistical analysis and synoptic climatology of u s heat waves
A Statistical Analysis and Synoptic Climatology of U.S. Heat Waves

Scott C. Runyon

and

Lance F. Bosart

Department of Earth and Atmospheric Sciences,

University at Albany, State University of New York

introduction
Introduction:

Why study heat waves?

  • Heat waves are a major contributor to weather–related fatalities
  • Understanding the characteristics of heat waves would lead to improved forecasts
  • These forecasts may become more critical given the possibility of an increase in the frequency and intensity of heat waves
introduction cont
Introduction (cont.):
  • Previous work has largely focused on specific heat wave events (e.g. July 1995) or extended “heat wave-droughts” (e.g. 1980 & 1988)
  • Published synoptic climatologies have been limited in scope to the Midwest or Great Plains (e.g. Chang & Wallace, 1984)
purpose
Purpose:
  • Document differences in heat waves as a function of both season and region
  • Understand both the dynamical and thermo-dynamical contributions to regional heat waves
  • Resolve annual and decadal trends in heat wave frequency
overview
Overview:
  • Methodology/Definitions
  • Results
    • Skewed datasets
    • Northeast heat wave statistics
    • Case Study: 7-11 June 1984
  • Summary
    • Conclusions
    • Future Work
methodology
Methodology:
  • Temperature data was extracted from the National Climatic Data Center’s (NCDC) high resolution surface dataset
  • Database contains daily high temperatures for 54 stations over a 54-year period (1948-2001)
  • Surface stations were selected on the basis of dataset continuity and geographical coverage
methodology cont
Methodology (cont.):

Stations in Database

methodology cont8
Methodology (cont.):
  • Definitions:
    • An anomalously hot day was initially defined as a day having a high temperature at least 2 standard deviations (σ) above the normal high temperature for the date
    • This definition was later changed to a day having a high temperature above the climatological 97.5 percentile threshold for the date
    • A heat wave was defined as three or more consecutive anomalously hot days – no matter the season
methodology cont9
Methodology (cont.)
  • Regions mirror the NCDC Standard Regions for Temperature and Precipitation
  • Heat waves were considered regional when two or more stations within that region had overlapping warm days
  • All anomalously hot days, heat waves, and regional heat waves were identified for each station and each region across the country
results
Results

“Skewness” of Daily High Temperature Data

  • Initially using a 2σ high temperature anomaly to define a hot day  a surprising amount of variability in number of heat waves identified from one city to the next
  • A serendipitous discovery: a discrepancy between number of anomalously cold days and the number of anomalously hot days for most stations in the dataset
results top ten skewed cities
Results: Top Ten “Skewed” Cities

Positively Skewed

Negatively Skewed

most negatively skewed city denver
Most Negatively Skewed City: Denver
  • In 54-year dataset, only 49 days met old criteria as anomalously hot days (T≥2σ)
  • Only 2 heat waves were identified in the entire dataset
slide13

Denver, CO Daily High Temperatures – Spring

Mean + 2σ

Mean

Mean - 2σ

March 1 – May 31

most positively skewed city los angeles
Most Positively Skewed City: Los Angeles
  • In 54-year dataset, 989 days met old criteria as anomalously hot days (T≥2σ)
  • Hence, over 130 heat waves were identified in the dataset
new method
New Method:
  • An anomalously hot day is now defined as a day having a high temperature above the daily climatological 97.5 percentile threshold
  • Heat wave definitions remained unchanged
  • Updated Figures:
    • DNR: 736 Days, 41 Heat Waves
    • LAX: 669 Days, 66 Heat Waves
stat analysis of northeast heat waves32
Stat. Analysis of Northeast Heat Waves

Results:

  • A trend toward more Winter and Spring heat waves with time
  • A trend toward fewer Summer and Autumn heat waves with time
  • Decades of the 1950’s, 1980’s and 1990’s had the highest frequency of heat waves
a case study 7 11 june 1984
A Case Study: 7-11 June 1984
  • A long-lasting, region-wide Northeast heat wave
  • Both thermodynamic and dynamic signatures can be seen
june 4 1984
June 4, 1984

ALB: 80° F

LGA: 85° F

BOS: 85° F

june 5 1984
June 5, 1984

ALB: 84° F

LGA: 88° F

BOS: 89° F

june 6 1984
June 6, 1984

ALB: 81° F

LGA: 88° F

BOS: 70° F

june 7 1984
June 7, 1984

ALB: 89° F

LGA: 93° F

BOS: 89° F

june 8 1984
June 8, 1984

ALB: 93° F

LGA: 96° F

BOS: 97° F

june 9 1984
June 9, 1984

ALB: 93° F

LGA: 95° F

BOS: 96° F

june 10 1984
June 10, 1984

ALB: 91° F

LGA: 95° F

BOS: 95° F

june 11 1984
June 11, 1984

ALB: 90° F

LGA: 96° F

BOS: 98° F

june 12 1984
June 12, 1984

ALB: 82° F

LGA: 84° F

BOS: 85° F

june 4 198443
June 4, 1984

ALB: 80° F

LGA: 85° F

BOS: 85° F

june 5 198444
June 5, 1984

ALB: 84° F

LGA: 88° F

BOS: 89° F

june 6 198445
June 6, 1984

ALB: 81° F

LGA: 88° F

BOS: 70° F

june 7 198446
June 7, 1984

ALB: 89° F

LGA: 93° F

BOS: 89° F

june 8 198447
June 8, 1984

ALB: 93° F

LGA: 96° F

BOS: 97° F

june 9 198448
June 9, 1984

ALB: 93° F

LGA: 95° F

BOS: 96° F

june 10 198449
June 10, 1984

ALB: 91° F

LGA: 95° F

BOS: 95° F

june 11 198450
June 11, 1984

ALB: 90° F

LGA: 96° F

BOS: 98° F

june 12 198451
June 12, 1984

ALB: 82° F

LGA: 84° F

BOS: 85° F

case study results
Case Study Results
  • Origin of hot air: Rockies (elevated heat source)
  • Downsloping plays a role:
    • Westward extension of the Bermuda High into Southeast allowed for west-northwesterly flow throughout period
    • Warm air coming off Rockies
  • Anticylonic shear side of jet: subsidence
  • June 9th: Northeast located in the equator-ward jet exit region: enhanced subsidence
conclusions
Conclusions:
  • From initial heat-wave identifying methodology: daily max temperatures are not normally distributed
  • Stations located adjacent to cool (warm) water seem to have positively (negatively) skewed high temperature data
  • Most stations have a cool bias
conclusions cont
Conclusions (cont.) :
  • From Northeast statistics:
    • Positive heat wave trend in Winter and Spring
    • Negative heat wave trend in Summer and Autumn
  • From case study:
    • Heat waves can have both dynamic and thermodynamic aspects
    • Local wind direction is important
conclusions cont55
Conclusions (cont.):
  • Future Work:
    • Apply statistical methods to other regions

Create composite analyses to:

    • Illustrate typical synoptic signatures of heat waves in each season
    • Determine regional “flavor” of heat waves